alihmaou/Agents_MCP_Hackathon_Tools_List huggingface dataset:
Tool: AI_Marketing_Content_Creator_enhanced_batch_generation
Space: AI-Marketing-Content-Creator
Description: Generate strategic variations for A/B testing
Inputs: prompt, variation_type, count, num_steps
Tags: gradio, mcp-server-track, ai-agents, content-creation, Agents-MCP-Hackathon, social-media, modal, mistral, anthropic, mcp-server, region:us
Tool: AI_Marketing_Content_Creator_social_media_generation
Space: AI-Marketing-Content-Creator
Description: Generate images for multiple social media platforms with correct resolutions
Inputs: prompt, platforms, num_steps
Tags: gradio, mcp-server-track, ai-agents, content-creation, Agents-MCP-Hackathon, social-media, modal, mistral, anthropic, mcp-server, region:us
Tool: Router_MCP_handle_keyword_mcp_search
Space: Router-MCP
Description: Handle keyword-based search for MCP servers across selected sources. If the client (such as Cursor or Claude) encounters a task it cannot handle, it can use MCP to search for other tools that can. Use this search when you know the specific name or keywords of the MCP Server you're looking for. Returns: A dictionary containing the search results with the following keys:, results: List of found MCP servers with their configurations. Each configuration can be added to the MCP Client's config file to register the server., total: Total number of results, search_type: Type of search performed ("keyword")
Inputs: query, hf, sm, limit, os_type
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: Router_MCP_handle_semantic_mcp_search
Space: Router-MCP
Description: Handle semantic embedding-based search for MCP servers across selected sources. If the client (such as Cursor or Claude) encounters a task it cannot handle, it can use MCP to search for other tools that can. Use this search when your query is more abstract or conceptual, as it can understand the meaning and context of your request. Returns: A dictionary containing the search results with the following keys:, results: List of found MCP servers with their configurations and similarity scores. Each configuration can be added to the MCP Client's config file to register the server., total: Total number of results, search_type: Type of search performed ("semantic")
Inputs: query, hf, sm, limit, os_type
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: modal_sandbox_mcp_launch_sandbox
Space: modal_sandbox_mcp
Description: Launches a new Modal sandbox with specified configuration. Useful for creating isolated Python environments, running code with specific dependencies, testing in clean environments, executing long-running tasks, running GPU-accelerated workloads, training machine learning models, and running inference on large models. Returns: JSON string containing sandbox_id, status, python_version and installed packages
Inputs: app_name, python_version, pip_packages, apt_packages, timeout_seconds, cpu, memory, secrets, volumes, inject_predefined_secrets, workdir, gpu_type, gpu_count
Tags: gradio, mcp-server-track, modal, sandbox, mcp, cloud-computing, python, ml, hackathon, vector-database, embeddings, workflow-automation, mcp-server, region:us
Tool: modal_sandbox_mcp_terminate_sandbox
Space: modal_sandbox_mcp
Description: Terminates a Modal sandbox by its ID. Useful for stopping running sandboxes that are no longer needed, cleaning up resources, forcefully ending long-running or stuck sandboxes, and managing sandbox lifecycle. Returns: JSON string containing success status and termination message
Inputs: sandbox_id
Tags: gradio, mcp-server-track, modal, sandbox, mcp, cloud-computing, python, ml, hackathon, vector-database, embeddings, workflow-automation, mcp-server, region:us
Tool: modal_sandbox_mcp_list_sandboxes
Space: modal_sandbox_mcp
Description: Lists all Modal sandboxes for a specific app namespace and their current status. Useful for monitoring active Modal sandbox environments, checking which sandboxes are running, and getting sandbox IDs for further management operations. Returns: JSON string containing list of sandboxes with their IDs and status
Inputs: app_name
Tags: gradio, mcp-server-track, modal, sandbox, mcp, cloud-computing, python, ml, hackathon, vector-database, embeddings, workflow-automation, mcp-server, region:us
Tool: modal_sandbox_mcp_execute_command
Space: modal_sandbox_mcp
Description: Executes a command in a specified Modal sandbox environment. Useful for running arbitrary commands in isolated environments, testing scripts and programs, executing programs with specific dependencies, debugging environment-specific issues, and running automated tests in isolation. Note: Commands are processed using shlex.split() and executed via subprocess.run(), so shell operators like &&, ||, |, >, < are not supported. Use separate command calls for chaining operations. Returns: JSON string containing stdout, stderr, returncode and execution time
Inputs: sandbox_id, command, timeout_seconds
Tags: gradio, mcp-server-track, modal, sandbox, mcp, cloud-computing, python, ml, hackathon, vector-database, embeddings, workflow-automation, mcp-server, region:us
Tool: modal_sandbox_mcp_push_file_to_sandbox
Space: modal_sandbox_mcp
Description: Copies a file from the local filesystem to a Modal sandbox. Useful for uploading input files, transferring configuration files, setting up sandbox environments, and deploying code to sandboxes. Returns: JSON string containing success status, paths and file size
Inputs: sandbox_id, local_path, sandbox_path, read_file_mode, writefile_mode
Tags: gradio, mcp-server-track, modal, sandbox, mcp, cloud-computing, python, ml, hackathon, vector-database, embeddings, workflow-automation, mcp-server, region:us
Tool: modal_sandbox_mcp_pull_file_from_sandbox
Space: modal_sandbox_mcp
Description: Copies a file from a Modal sandbox to the local filesystem. Useful for retrieving output files, backing up sandbox data, analyzing sandbox-generated content locally, and debugging sandbox operations. Returns: JSON string containing success status, paths and file size
Inputs: sandbox_id, sandbox_path, local_path
Tags: gradio, mcp-server-track, modal, sandbox, mcp, cloud-computing, python, ml, hackathon, vector-database, embeddings, workflow-automation, mcp-server, region:us
Tool: modal_sandbox_mcp_list_directory_contents
Space: modal_sandbox_mcp
Description: Lists contents of a directory in the sandbox. Useful for exploring sandbox filesystem structure, verifying file operations, debugging file-related issues, and managing sandbox content. Returns: JSON string containing list of directory contents
Inputs: sandbox_id, path
Tags: gradio, mcp-server-track, modal, sandbox, mcp, cloud-computing, python, ml, hackathon, vector-database, embeddings, workflow-automation, mcp-server, region:us
Tool: modal_sandbox_mcp_make_directory
Space: modal_sandbox_mcp
Description: Creates a new directory in the sandbox. Useful for setting up directory structures, preparing for file operations, and organizing sandbox content. Returns: JSON string containing success status and created path
Inputs: sandbox_id, path, parents
Tags: gradio, mcp-server-track, modal, sandbox, mcp, cloud-computing, python, ml, hackathon, vector-database, embeddings, workflow-automation, mcp-server, region:us
Tool: modal_sandbox_mcp_remove_path
Space: modal_sandbox_mcp
Description: Removes a file or directory from the sandbox. Useful for cleaning up temporary files, removing unwanted content, and managing sandbox storage. Returns: JSON string containing success status and removed path
Inputs: sandbox_id, path, recursive
Tags: gradio, mcp-server-track, modal, sandbox, mcp, cloud-computing, python, ml, hackathon, vector-database, embeddings, workflow-automation, mcp-server, region:us
Tool: modal_sandbox_mcp_read_file_content_from_sandbox
Space: modal_sandbox_mcp
Description: Reads the content of a file in the sandbox. Useful for viewing file contents without downloading, debugging sandbox operations, checking operation results, and quick file inspection. Returns: JSON string containing the file content
Inputs: sandbox_id, path
Tags: gradio, mcp-server-track, modal, sandbox, mcp, cloud-computing, python, ml, hackathon, vector-database, embeddings, workflow-automation, mcp-server, region:us
Tool: modal_sandbox_mcp_write_file_content_to_sandbox
Space: modal_sandbox_mcp
Description: Writes content to a file in a Modal sandbox. Useful for writing code, text, or any other content, rapid prototyping, creating boilerplate code, implementing algorithms, and generating test cases. Returns: JSON string containing success status and written file path
Inputs: sandbox_id, sandbox_path, content
Tags: gradio, mcp-server-track, modal, sandbox, mcp, cloud-computing, python, ml, hackathon, vector-database, embeddings, workflow-automation, mcp-server, region:us
Tool: modal_sandbox_mcp_add_problem_solving_template
Space: modal_sandbox_mcp
Description: Adds a problem-solving template to the database that defines a sequence of Modal sandbox tool operations to solve a specific task. Templates provide guidance on tool orchestration, required variables, volumes, and dependencies for common use cases like resume screening, code analysis, data processing etc. For example, a resume screening template would specify: 1. Required volumes for storing resumes and results 2. Python packages needed (e.g., PyPDF2, spacy) 3. Sequence of sandbox operations: - Launch sandbox with specific memory/CPU requirements - Create directories for resumes and results - Push resume files to sandbox - Execute NLP processing commands - Pull results from sandbox - Clean up resources. Returns: JSON string containing:, success (bool): Whether template was added successfully, template_id (str): Unique ID of added template, message (str): Status message
Inputs: name, description, required_tools, required_volumes, required_packages, tool_sequence, resource_requirements, example_usage
Tags: gradio, mcp-server-track, modal, sandbox, mcp, cloud-computing, python, ml, hackathon, vector-database, embeddings, workflow-automation, mcp-server, region:us
Tool: modal_sandbox_mcp_search_problem_solving_templates
Space: modal_sandbox_mcp
Description: Performs semantic search across problem-solving templates to find the most relevant ones for a given task. Useful for discovering existing solutions, finding similar use cases, and reusing proven template patterns. The search uses semantic similarity to match task descriptions with template descriptions and requirements. Returns: JSON string containing an array of template matches, each with:, template_id (str): Unique identifier of the template, name (str): Template name, description (str): Template description, similarity_score (float): Semantic similarity score (0-1), required_tools (List[str]): Tools needed by the template, required_packages (List[str]): Dependencies needed, resource_requirements (Dict): CPU/memory/GPU requirements
Inputs: task_description, top_k
Tags: gradio, mcp-server-track, modal, sandbox, mcp, cloud-computing, python, ml, hackathon, vector-database, embeddings, workflow-automation, mcp-server, region:us
Tool: TrackMate_AI_MCP_Server_place_new_order
Space: TrackMate-AI-MCP-Server
Description: Place a new order in the ERP system, after ensuring there are no global disruptions affecting the shipment. Returns: New order information
Inputs: customer_id, items, shipping_address, shipping_country, destination_country, previous_order_id, param_6
Tags: gradio, mcp-server, region:us
Tool: hermes_activate_monitoring
Space: hermes
Description: TOOL: Activate periodic monitoring for a validated API configuration. PURPOSE: Start automated recurring API calls based on a previously validated configuration. This is STEP 2 of the monitoring setup process. PREREQUISITE: Must call validate_api_configuration() first and obtain a config_id from successful validation. Make sure that the sample_response is what you expect to see before proceeding with this function. WORKFLOW: 1. First call validate_api_configuration() to get config_id 2. If validation successful, call this function with the config_id 3. Monitoring will run automatically according to the validated schedule ARGUMENTS: - config_id: The ID from successful validate_api_configuration() execution (required) - mcp_api_key: User's MCP API key for verification (must match validation step). Input Examples: 1. Activate scheduler for stock monitoring: config_id: 123456789 mcp_api_key: "your_mcp_key_here" 2. Activate scheduler for weather alerts: config_id: 987654321 mcp_api_key: "your_mcp_key_here" NOTE: The config_id must be obtained from a successful validate_api_configuration() response. The mcp_api_key must match the one used during validation. Returns: Dictionary with success status and scheduling details, , {, True,, "Scheduler activated for 'NVDA Stock Price'",, 123,, 20,, "2025-06-11T12:00:00Z",, "2025-06-04T12:20:00Z", }, If config_id not found or invalid, returns success=False with error message
Inputs: config_id, mcp_api_key
Tags: gradio, Agents-MCP-Hackathon, mcp-server-track, mcp-server, region:us
Tool: hermes_retrieve_monitored_data
Space: hermes
Description: TOOL: Retrieve monitored data for a specific API configuration. PURPOSE: Fetch the latest monitored data for a given configuration ID. This is STEP 3 of the monitoring setup process. PREREQUISITE: Must call validate_api_configuration() first and obtain a config_id from successful validation, then activate_monitoring() to start monitoring. This function can be called at any time after monitoring activation to retrieve the latest data collected by the monitoring system. ARGUMENTS: - config_id: The ID of the API configuration to retrieve data for (required) - mcp_api_key: User's MCP API key for verification (must match validation step). - mode: Data return mode - "summary" (LLM-optimized), "details" (full responses, minimal metadata), "full" (everything) Input Examples: 1. Retrieve data for stock monitoring: config_id: 123456789 mcp_api_key: "your_mcp_key_here" 2. Retrieve data for weather alerts: config_id: 987654321 mcp_api_key: "your_mcp_key_here". Returns: Dictionary with monitoring status in one of three formats based on mode parameter, , {, True,, "Weather Alert Monitor",, {, "active",  // "active", "inactive", "good",    // "good", "degraded", "no_data", 15,, 93.3,, "2025-06-05T15:20:00",, "2025-06-05T15:20:00", },, [, {, "2025-06-05T15:20:00",, true,, null,, "{'alerts': [{'type': 'tornado'}]}..."  // truncated to 150 characters, }, // ... up to 5 most recent calls, ],, 15,, {, 20,, false, }, }, , {, True,, "Weather Alert Monitor",, "active",, 15,, 93.3,, [, {, "2025-06-05T15:20:00",, true,, {...},  // full response data, null, }, // ... up to 10 most recent calls with full responses, ], }, , {, True,, "Weather Alert Monitor",, "Monitor severe weather alerts",, True,, False,, {...},, {...},, [...]  // all historical data, }, , {, False,, "Invalid config_id or mcp_api_key", }, If config_id not found or invalid, returns success=False with error message
Inputs: config_id, mcp_api_key, mode
Tags: gradio, Agents-MCP-Hackathon, mcp-server-track, mcp-server, region:us
Tool: LLM_GameMaster_MPC_Server_add_token_light
Space: LLM-GameMaster-MPC-Server
Description: Add light to a token. This will allow to show it inside the fog of war. It can be used to add light to items like torches or characters. It will be attached so that the light moves with the token.
Inputs: tab_id, item_id, light_radius
Tags: gradio, agent-demo-track, mcp-server-track, mcp-server, region:us
Tool: LLM_GameMaster_MPC_Server_insert_map
Space: LLM-GameMaster-MPC-Server
Description: Insert a map into the game. The available maps are: - FOREST: A forest with multiple paths and trees. - VILLAGE: A small village with 3 houses and a well in the middle. - LONELY_CABIN: A lonely cabin in the middle of a plain. - CABIN: The interior of a cabin. - BATTLE_ARENA: A roman battle arena with a central platform and walls around it.
Inputs: tab_id, map_type
Tags: gradio, agent-demo-track, mcp-server-track, mcp-server, region:us
Tool: TDAgentTools_query_rdap
Space: TDAgentTools
Description: Query RDAP to get information about Internet resources. The Registration Data Access Protocol (RDAP) is the successor to WHOIS. Like WHOIS, RDAP provides access to information about Internet resources (domain names, autonomous system numbers, and IP addresses).
Inputs: url_or_ip
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: TDAgentTools_get_geolocation
Space: TDAgentTools
Description: Get location information from an ip address. Returns the following information on an ip address: 1. IPv4 2. city 4. country_code 5. country_name 6. latitude 7. longitude 8. postal 9. state Example: >>> from pprint import pprint >>> pprint(get_location("103.100.104.0")) ... {'IPv4': '103.100.104.0', 'city': None, 'country_code': 'NZ', 'country_name': 'New Zealand', 'latitude': -41, 'longitude': 174, 'postal': None, 'state': None}
Inputs: ip
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: TDAgentTools_enumerate_dns
Space: TDAgentTools
Description: Enumerates information about a specific domain's DNS configuration. Information collected about the domain name: 1. A records: the IPv4 associated with the domain 2. AAAA records: the IPv6 associated with the domain 3. CAA records: used by owners to specify which Certificate Authorities are authorized to issue SSL/TLS certificates for their domains. 4. CNAME records: alias of one name to another - the DNS lookup will continue by retrying the lookup with the new name. 5. LOC records: geographic location associated with a domain name. 6. MX records: associated email servers to the domain. 7. NS records: DNS servers that are authoritative for a particular domain. These may be use to inquire information about the domain. 8. SOA records: defines authoritative information about a DNS zone, including zone transfers and cache expiration. 9. TXT records: used for domain verification and email security. 10. RP records: the responsible person for a domain. 11. SPF records: defines authorized email servers. 12. SRV records: specifies location of specific services (port and host) for the domain. 14. NSEC records: proves non-existence of DNS records and prevents zone enumeration. 15. RRSIG records: contains cryptographic signatures for DNSSEC-signed records, providing authentication and integrity. Example: >>> from pprint import pprint >>> pprint(enumerate_dns("youtube.com")) ... {'A': 'youtube.com. 300 IN A 142.250.200.142', 'AAAA': 'youtube.com. 286 IN AAAA 2a00:1450:4003:80f::200e', 'CAA': 'youtube.com. 14352 IN CAA 0 issue "pki.goog"', 'CNAME': None, 'LOC': None, 'MX': 'youtube.com. 300 IN MX 0 smtp.google.com.', 'NS': 'youtube.com. 21600 IN NS ns4.google.com.\n' 'youtube.com. 21600 IN NS ns1.google.com.\n' 'youtube.com. 21600 IN NS ns2.google.com.\n' 'youtube.com. 21600 IN NS ns3.google.com.', 'NSEC': None, 'RP': None, 'RRSIG': None, 'SOA': 'youtube.com. 60 IN SOA ns1.google.com. dns-admin.google.com. ' '766113658 900 900 1800 60', 'SPF': None, 'SRV': None, 'TXT': 'youtube.com. 3586 IN TXT "v=spf1 include:google.com mx -all"\n' 'youtube.com. 3586 IN TXT ' '"facebook-domain-verification=64jdes7le4h7e7lfpi22rijygx58j1"\n' 'youtube.com. 3586 IN TXT ' '"google-site-verification=QtQWEwHWM8tHiJ4s-jJWzEQrD_fF3luPnpzNDH-Nw-w"'}
Inputs: domain_name
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: TDAgentTools_scrap_subdomains_for_domain
Space: TDAgentTools
Description: Retrieves subdomains associated to a domain if any. The information retrieved from a domain is its subdomains provided they are the top 1000 subdomain prefixes as indicated by https://github.com/rbsec/dnscan/tree/master Importantly, it finds subdomains only if their prefixes are along the top 1000 most common. Hence, it may not yield all the subdomains associated to the domain. Example: >>> scrap_subdomains_for_domain("github.com") ... ['www.github.com', 'smtp.github.com', 'ns1.github.com', 'ns2.github.com','autodiscover.github.com', 'test.github.com', 'blog.github.com', 'admin.github.com', 'support.github.com', 'docs.github.com', 'shop.github.com', 'wiki.github.com', 'api.github.com', 'live.github.com', 'help.github.com', 'jobs.github.com', 'services.github.com', 'de.github.com', 'cs.github.com', 'fr.github.com', 'ssh.github.com', 'partner.github.com', 'community.github.com', 'mailer.github.com', 'training.github.com', ...]
Inputs: domain_name
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: TDAgentTools_retrieve_ioc_from_threatfox
Space: TDAgentTools
Description: Retrieves information about a potential IoC from ThreatFox. It may be used to retrieve information of indicators of compromise (IOCs) associated with malware, with the infosec community, AV vendors and cyber threat intelligence providers. Examples: >>> retrieve_ioc_from_threatfox("139.180.203.104") ... { "query_status": "ok", "data": [ { "id": "12", "ioc": "139.180.203.104:443", "threat_type": "botnet_cc", "threat_type_desc": "Indicator that identifies a botnet command&control...", "ioc_type": "ip:port", "ioc_type_desc": "ip:port combination that is used for botnet Command&..., "malware": "win.cobalt_strike", "malware_printable": "Cobalt Strike", "malware_alias": "Agentemis,BEACON,CobaltStrike", "malware_malpedia": "https:\/\/malpedia.caad.fkie.fraunhofer.de\/...", "confidence_level": 75, "first_seen": "2020-12-06 09:10:23 UTC", "last_seen": null, "reference": null, "reporter": "abuse_ch", "tags": null, "malware_samples": [ { "time_stamp": "2021-03-23 08:18:06 UTC", "md5_hash": "5b7e82e051ade4b14d163eea2a17bf8b", "sha256_hash": "b325c92fa540edeb89b95dbfd4400c1cb33599c66859....", "malware_bazaar": "https:\/\/bazaar.abuse.ch\/sample\/b325c...\/" }, ] } ] }
Inputs: potentially_ioc
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: TDAgentTools_send_email
Space: TDAgentTools
Description: Simulates sending an email from cert@company.com. This function takes email details, formats them into a standard email structure with headers and body, prints the formatted email to the console, and returns a success message. The sender is always set to cert@company.com.
Inputs: recipient, subject, message
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: ImageAlfred_privacy_preserve_image
Space: ImageAlfred
Description: Obscures specified objects in the input image based on a natural language prompt, using a privacy-preserving blur or distortion effect. This function segments the image to detect objects described in the `input_prompt` and applies a pixelation effect to those regions. It is useful in scenarios where sensitive content (e.g., faces, license plates, logos, personal belongings) needs to be hidden before sharing or publishing images. Returns: Binary image data of the modified image., , input_prompt = "faces, license plates, logos"
Inputs: input_img, input_prompt, privacy_strength, threshold
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: ImageAlfred_change_color_objects_hsv
Space: ImageAlfred
Description: Changes the hue and saturation of specified objects in an image using the HSV color space. This function segments image regions based on a user-provided text prompt and applies color transformations in the HSV color space. HSV separates chromatic content (hue) from intensity (value), making it more intuitive for color manipulation tasks. Use this method when: - You want to change the color of objects based on their hue and saturation. - You want to apply color transformations that are less influenced by lighting conditions or brightness variations. Returns: Base64-encoded string., , If user_input format is invalid, or image format is invalid or corrupted., If input_img is not a supported type or modal function returns unexpected type.
Inputs: input_img, user_input
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: ImageAlfred_change_color_objects_lab
Space: ImageAlfred
Description: Changes the color of specified objects in an image using the LAB color space. This function segments image regions based on a user-provided text prompt and applies color transformations in the LAB color space. LAB separates luminance (L) from color components (A for green-red, B for blue-yellow), making it more perceptually uniform and closer to how humans perceive color differences. Use this method when: - Precise perceptual color control is needed (e.g., subtle shifts in tone or matching specific brand colors). - Working in lighting-sensitive tasks where separating lightness from chroma improves quality. - You want color transformations that are less influenced by lighting conditions or brightness variations. OpenCV LAB Ranges: - L: 0-255 (lightness) - A: 0-255 (green-red, 128 = neutral) - B: 0-255 (blue-yellow, 128 = neutral) Common LAB color references: - Green: (L=?, A≈80, B≈128) - Red: (L=?, A≈180, B≈160) - Blue: (L=?, A≈128, B≈80) - Yellow: (L=?, A≈120, B≈180) - Purple: (L=?, A≈180, B≈100). Returns: Base64-encoded string, , If user_input format is invalid, a/b values are outside [0, 255] range, or image format is invalid or corrupted., If input_img is not a supported type or modal function returns unexpected type.
Inputs: input_img, user_input
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: healthify_me_mcp_search_food_products
Space: healthify-me-mcp
Description: Recherche des produits alimentaires dans la base de données OpenFoodFacts. Cette fonction effectue une recherche intelligente de produits alimentaires en analysant la requête utilisateur avec Mistral AI pour extraire le nom du produit et la marque, puis interroge la base DuckDB.
Inputs: query, max_results
Tags: gradio, mcp-server-track, Mistral, openfoodfacts, mcp-server, region:us
Tool: ThinkSquare_play_chess
Space: ThinkSquare
Description: Play a move in a chess game. Instructions for LLMs: Prerequisites: - User must be asked if they want to play as white or black. - If user chooses black, pass an empty string in the first move (for the engine to play as white). - If the user chooses white, LLMs must ask the user for a move and pass it in the first move (for the engine to play as black). - User must be asked if they want a board drawn. - If they do, pass `draw_board=True` to this function. - If they do not, pass `draw_board=False`. - If a move is provided, it must be in long algebraic notation (e.g., "e4", "Nf3", "Bb5"). To start a new game: - Pass empty string for fen. - Pass empty string for engine to play as white. - Pass a move in long algebraic notation (e.g., "e4", "Nf3", "Bb5") for engine to play as black. To coninue a game: - Pass the FEN string representing the board state prior to the user's last move. - Pass a move in long algebraic notation (e.g., "e4", "Nf3", "Bb5") for engine to play the next move. About rendering: - LLMs must use ascii as render_mode unless otherwise specified by user. While rendering ascii, LLMs must use monospaced font. - LLMs must explicitly pass render_mode = "ascii" to this function if they want to render the board in ASCII format. Returns: The best move played by the engine, the updated board state in FEN notation, and  a board representation if draw_board is True else None.
Inputs: move, fen, draw_board, render_mode
Tags: gradio, mcp-server-track, modal, sambanova, mcp-server, region:us
Tool: ThinkSquare_annotate_pgn_file
Space: ThinkSquare
Description: Annotate a chess game from a PGN file. This function takes a PGN file and annotates the chess game using an engine. Instructions for LLMs: - LLMs must not use this function. Use the string-based `annotate_pgn` instead.
Inputs: file, depth_level, style
Tags: gradio, mcp-server-track, modal, sambanova, mcp-server, region:us
Tool: ThinkSquare_annotate_pgn
Space: ThinkSquare
Description: Annotate a chess game with engine analysis. This function takes a chess game (PGN) in string format. Instructions for LLMs: - Do not send the file name or file path as an arument. - LLMs must send the chess game in PGN format as a string. - If the user provides a file, read the file and extract the PGN content. - LLMs should display the annotated game in a Markdown code block with the label "Annotated PGN". Returns: The annotated game in PGN format as a string.
Inputs: pgn_input, analysis_time_per_move, style
Tags: gradio, mcp-server-track, modal, sambanova, mcp-server, region:us
Tool: ThinkSquare_suggest_move
Space: ThinkSquare
Description: Suggest a move for the given FEN position. This function can be used to give a hint to the user about the best move to play. This function takes a FEN string representing the current board state and returns the best move in SAN format. Returns: The best move in SAN format.
Inputs: fen
Tags: gradio, mcp-server-track, modal, sambanova, mcp-server, region:us
Tool: code_analysis_mcp_code_analysis_score
Space: code-analysis-mcp
Description: Analyzes the provided code string using multiple AI providers and returns an averaged score across vulnerability, style, and quality. Returns: A dictionary containing the averaged vulnerability, style, and quality scores,, or an error message if all providers fail.
Inputs: code
Tags: gradio, mcp-server-track, code-analysis, openai, anthropic, mistral, mcp-server, region:us
Tool: E_commerce_analyst_MCP_generate_code_wrapper
Space: E-commerce_analyst_MCP
Description: ### `generate_code_wrapper(user_request: str)` - **Purpose**: Generate Python code based on user request - **Parameters**: `user_request` - Textual description of the analysis - **Returns**: Generated code and status message - **Use Case**: AI-powered code generation for data analysis
Inputs: user_request
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: E_commerce_analyst_MCP_generate_graph_wrapper
Space: E-commerce_analyst_MCP
Description: ### `generate_graph_wrapper(graph_type: str, data_json: str)` - **Purpose**: Create visualizations using matplotlib - **Parameters**: `graph_type` - Type of chart (bar, line, pie, scatter) - **Returns**: Image file path and status - **Use Case**: Creating charts and graphs for presentations
Inputs: graph_type, data_json
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: E_commerce_analyst_MCP_embed_text_modal_api
Space: E-commerce_analyst_MCP
Description: This function allows you to send a list of text to be embeded by our external model. the format should be the following: [ "text_to_embed 1", "text_to_embed 2", "text_to_embed 3", ... ] the return is the vector embedding corresponding to the text you input or a list of vector in case you gave a list of text. thoses vectors are compatible with the one you can find in the database and can be used to be compared with vectors you'll get by querying the database.
Inputs: text
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: E_commerce_analyst_MCP_query_and_generate_graph_wrapper
Space: E-commerce_analyst_MCP
Description: Executes a SQL query and generates a graph visualization from the results. This function is particularly useful when query results are too large for the context window or when visual representation of data is preferred over tabular format. Returns: A tuple containing:, image_path (str or None): Path to the generated graph image if successful, None if failed, status (str): Status message indicating success or error details
Inputs: query, graph_type
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: E_commerce_analyst_MCP_do_annova
Space: E-commerce_analyst_MCP
Description: this function runs the annova on the dataset and render the associated F_score and p_value table_name is the name of the table on which you want to run the ANOVA the selected table MUST have the following signature: groups | measurement exemple with the product_type_age table: type | age ---------- 'Coat', '36' 'Coat', '36' 'Hat/beanie', '32' ... min_sample_size is used to exclude categories that does not have enough measurement. default = 0: all categories are selected return type is: dict { "F-statistic": round(f_stat, 3), "p-value": round(p_value, 3) }
Inputs: table_name, min_sample_size
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: E_commerce_analyst_MCP_do_tukey_test
Space: E-commerce_analyst_MCP
Description: this function runs a Tukey's HSD (Honestly Significant Difference) test — a post-hoc analysis following ANOVA. It tells you which specific pairs of groups differ significantly in their means IT is meant to be used after you run a successful anova and you obtain sgnificant F-satatistics and p-value table_name is the name of the table on which you want to run the ANOVA the selected table MUST have the following signature: groups | measurement exemple with the product_type_age table: type | age ---------- 'Coat', 36 'Coat', 36 'Hat/beanie', 32 ... min_sample_size is used to exclude categories that does not have enough measurement. default = 0: all categories are selected the return result is the raw dataframe that correspond to the pair wize categorie that reject the hypothesis of non statistically difference between two group the signature of the dataframe is the following: group1 | group2 | meandiff p-adj | lower | upper | reject (only true)
Inputs: table_name, min_sample_size
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: E_commerce_analyst_MCP_do_tsne_embedding
Space: E-commerce_analyst_MCP
Description: this tool allow to run a TSNE dimensionality reduction algorythme and a clustering (HDBSCAN) on top of that. the input query, is a sql query that MUST return a table with at least the item id and the corresponding embeddding. FOR COMPUTATIONAL PURPOSE, THE QUERY YOU SEND MUST NOT RETURN A TABLE GREATER THAN 500 OUTPUT ROWS exemple: result = db_connection.read_only_query(query) result shape: article_id | embedding 0125456    | [0.3, 0.5 ...] the return is a dictionnary that has the following format: return { "ids": ids, "x_axis": tsne_projection_x_list, "y_axis": tsne_projection_y_list, "labels": labels }
Inputs: query
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: E_commerce_analyst_MCP_do_vector_centroid
Space: E-commerce_analyst_MCP
Description: this tool allow you to compute the centroid of a list of embedding vectors the input query, is a sql query that MUST return a table with only 1 column, the embeddings. exemple: result = db_connection.read_only_query(query) result shape: embedding [0.3, 0.5 ...] the return value is the computed centroid vector, that you can use to work with.
Inputs: query
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: E_commerce_analyst_MCP_get_mcp_server_instructions
Space: E-commerce_analyst_MCP
Description: Returns comprehensive usage guidelines and documentation for all MCP server functions. Call this function first to understand available tools, workflows, and best practices. This function provides: - Complete function documentation - Recommended workflows - Best practices for MCP clients - Database schema information - Statistical analysis guidelines
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: htrflow_mcp_htrflow_file
Space: htrflow_mcp
Description: Process HTR and return a formatted file for download. Returns: File path for direct download via gr.File (server_name/gradio_api/file=/tmp/gradio/{temp_folder}/{file_name})
Inputs: image_path, document_type, output_format, custom_settings, server_name
Tags: gradio, mcp-server-track, htrflow, htr, ocr, api, mcp-server, region:us
Tool: htrflow_mcp_htrflow_visualizer
Space: htrflow_mcp
Description: Visualize HTR results by overlaying text regions and polygons on the original image. Returns: File path to the generated visualization imagegenerated visualization image for direct download via gr.File (server_name/gradio_api/file=/tmp/gradio/{temp_folder}/{file_name}), https://gabriel-htrflow-mcp.hf.space/gradio_api/file=/tmp/gradio/34d5c1a8b7d5445469c4f7c638c490e0e3046b3008a0182f89c688b1b42d139b/htr_visualization.png
Inputs: image_path, htr_document_path, server_name
Tags: gradio, mcp-server-track, htrflow, htr, ocr, api, mcp-server, region:us
Tool: Vibe_Jamming_MCP_wav_to_musicxml
Space: Vibe-Jamming-MCP
Description: Convert a WAV audio file to a MusicXML score using pitch detection. Returns: File path to the generated MusicXML file., , If the MIDI file could not be generated.
Inputs: wav_path
Tags: gradio, Agents-MCP-Hackathon, mcp-server-track, mcp-server, region:us
Tool: Vibe_Jamming_MCP_render_musicxml_via_verovio_api
Space: Vibe-Jamming-MCP
Description: Render a MusicXML file to an SVG score preview using the Verovio API. Returns: HTML string containing base64-encoded SVG score image, or error message on failure.
Inputs: musicxml_path
Tags: gradio, Agents-MCP-Hackathon, mcp-server-track, mcp-server, region:us
Tool: Vibe_Jamming_MCP_generate_music_from_hum
Space: Vibe-Jamming-MCP
Description: Generate music from a humming audio file and a style prompt using an external MusicGen API. Returns: Path to the generated WAV music file, or error message on failure.
Inputs: melody_file, prompt
Tags: gradio, Agents-MCP-Hackathon, mcp-server-track, mcp-server, region:us
Tool: Vibe_Jamming_MCP_generate_score_from_audio
Space: Vibe-Jamming-MCP
Description: Generate a MusicXML score from an input audio (.wav) file. Returns: File path to the generated MusicXML file, or error message on failure.
Inputs: wav_file
Tags: gradio, Agents-MCP-Hackathon, mcp-server-track, mcp-server, region:us
Tool: AudioEditor_update_duration_info
Space: AudioEditor
Description: Update the duration info when a new file is uploaded This function is designed for UI purposes only to update Gradio interface elements when a new audio file is uploaded. It should NOT be used by agents or automated systems as it returns Gradio update objects for slider components. Agents should use the underlying audio utility functions directly instead.
Inputs: audio_file
Tags: gradio, gradio-mcp-server, audio-processing, mcp-server-track, mcp-server, region:us
Tool: AudioEditor_process_cut_audio
Space: AudioEditor
Description: Process audio cutting to extract a segment from an audio file. This function loads an audio file, validates the time parameters, and cuts out a specific segment between the start and end times. It handles various audio formats and provides detailed error messages for troubleshooting. Returns: A tuple containing:, First element: Either a tuple of (sample_rate: int, audio_data: array), for the cut audio segment, or None if an error occurred, Second element: A status message string indicating success with details, or error information, , result, status = process_cut_audio("/path/to/audio.mp3", 10.5, 25.0), , sample_rate, audio_data = result, {status}"), , {status}"), , Time parameters are automatically validated and clamped to valid ranges, Start time must be less than end time after validation, Output audio data maintains the original sample rate, Function returns user-friendly status messages for UI display
Inputs: audio_file, _start_time, _end_time
Tags: gradio, gradio-mcp-server, audio-processing, mcp-server-track, mcp-server, region:us
Tool: AudioEditor_apply_normalization
Space: AudioEditor
Description: Apply audio normalization to adjust the peak level of an audio file. This function loads an audio file and applies normalization to adjust the peak audio level to a specified target level in decibels (dB). It provides before and after statistics to show the effect of the normalization process. Returns: A tuple containing:, First element: Either a tuple of (sample_rate: int, normalized_audio_data: array), for the normalized audio result, or None if an error occurred, Second element: A status message string showing before/after peak levels, and success/error information, , result, status = apply_normalization("url/to/audio.mp3", -6.0), , sample_rate, audio_data = result, {status}"), , {status}"), , Target level is specified in decibels (dB), Common target levels: -6dB (moderate), -3dB (loud), -12dB (quiet), Positive target levels will amplify audio and may cause clipping, Negative target levels will reduce audio volume, Function preserves original sample rate and audio format, Returns comparison statistics showing original vs normalized peak levels
Inputs: audio_file, target_level
Tags: gradio, gradio-mcp-server, audio-processing, mcp-server-track, mcp-server, region:us
Tool: AudioEditor_apply_volume_adjustment
Space: AudioEditor
Description: Apply volume adjustment to an audio file using gain in decibels. This function loads an audio file and applies a volume adjustment by the specified gain amount in decibels. Positive values increase volume, negative values decrease volume. The function also detects potential audio clipping when volume is increased. Returns: A tuple containing:, First element: Either a tuple of (sample_rate: int, adjusted_audio_data: array), for the volume-adjusted audio result, or None if an error occurred, Second element: A status message string indicating success with gain applied, and clipping warning if detected, or error information, , result, status = apply_volume_adjustment("url/to/audio.mp3", -3.0), , sample_rate, audio_data = result, {status}"), , {status}"), , Gain is specified in decibels (dB): +6dB doubles volume, -6dB halves volume, Positive gain values may cause clipping (distortion) if audio becomes too loud, Function automatically detects and warns about clipping, Preserves original sample rate and audio format, Safe range is typically -20dB to +6dB to avoid quality issues
Inputs: audio_file, gain_db
Tags: gradio, gradio-mcp-server, audio-processing, mcp-server-track, mcp-server, region:us
Tool: AudioEditor_apply_fades
Space: AudioEditor
Description: Apply fade-in and fade-out effects to an audio file. This function loads an audio file and applies smooth fade-in and/or fade-out effects to eliminate abrupt starts/stops and create professional-sounding transitions. Fade effects gradually increase or decrease volume over the specified time periods. Returns: A tuple containing:, First element: Either a tuple of (sample_rate: int, faded_audio_data: array), for the fade-processed audio result, or None if an error occurred, Second element: A status message string showing applied fade durations, or error information, , result, status = apply_fades("url/to/audio.mp3", 1000, 2000), , sample_rate, audio_data = result, {status}"), , {status}"), , Fade durations are specified in milliseconds (1000ms = 1 second), Set either parameter to 0 to skip that fade effect, Fade-in gradually increases volume from silence at the beginning, Fade-out gradually decreases volume to silence at the end, Typical fade durations: 100-500ms (quick), 1000-3000ms (smooth), Preserves original sample rate and audio format, Fades are applied as smooth linear or exponential curves
Inputs: audio_file, fade_in_ms, fade_out_ms
Tags: gradio, gradio-mcp-server, audio-processing, mcp-server-track, mcp-server, region:us
Tool: AudioEditor_apply_reverse
Space: AudioEditor
Description: Reverse the playback direction of an audio file. This function loads an audio file and reverses the audio data so that it plays backwards. This creates a reverse playback effect commonly used for artistic purposes, sound design, or audio analysis. Returns: A tuple containing:, First element: Either a tuple of (sample_rate: int, reversed_audio_data: array), for the reversed audio result, or None if an error occurred, Second element: A status message string indicating successful reversal, or error information, , result, status = apply_reverse("url/to/audio.mp3"), , sample_rate, audio_data = result, {status}"), , {status}"), , Reverses the entire audio file from end to beginning, Preserves original sample rate, duration, and audio quality, Commonly used for creative effects, sound design, or subliminal messaging detection, The reversed audio will have the same duration as the original, All audio characteristics (pitch, timbre) are preserved but played backwards, Works with both mono and stereo audio files
Inputs: audio_file
Tags: gradio, gradio-mcp-server, audio-processing, mcp-server-track, mcp-server, region:us
Tool: AudioEditor_apply_speed_adjustment
Space: AudioEditor
Description: Apply speed adjustment to an audio file, changing playback speed and pitch. This function loads an audio file and adjusts its playback speed by the specified factor. Speed changes affect both duration and pitch - faster speeds increase pitch and reduce duration, while slower speeds decrease pitch and increase duration. Returns: A tuple containing:, First element: Either a tuple of (sample_rate: int, speed_adjusted_audio_data: array), for the speed-adjusted audio result, or None if an error occurred, Second element: A status message string showing speed factor and duration change, or error information, , result, status = apply_speed_adjustment("url/to/audio.mp3", 1.5), , sample_rate, audio_data = result, {status}"), , {status}"), , Speed factor affects both playback speed and pitch (chipmunk/slow-motion effect), Values > 1.0 increase speed and pitch, reduce duration, Values < 1.0 decrease speed and pitch, increase duration, Common values: 0.5 (half speed), 1.25 (25% faster), 2.0 (double speed), Extreme values (< 0.25 or > 4.0) may result in poor audio quality, For pitch-preserving speed changes, use time-stretching instead, Preserves original sample rate but changes audio duration
Inputs: audio_file, speed_factor
Tags: gradio, gradio-mcp-server, audio-processing, mcp-server-track, mcp-server, region:us
Tool: AudioEditor_apply_silence_trimming
Space: AudioEditor
Description: Trim silence from the beginning and end of an audio file. This function loads an audio file and automatically removes silent or very quiet sections from the beginning and end based on a specified volume threshold. This is useful for cleaning up recordings and removing unwanted quiet sections. Returns: A tuple containing:, First element: Either a tuple of (sample_rate: int, trimmed_audio_data: array), for the silence-trimmed audio result, or None if an error occurred, Second element: A status message string showing original and new duration, or error information, , result, status = apply_silence_trimming("url/to/audio.mp3", -40.0), , sample_rate, audio_data = result, {status}"), , {status}"), , Threshold is specified in decibels (dB) - more negative values = quieter threshold, Common thresholds: -30dB (conservative), -40dB (moderate), -60dB (aggressive), Only trims from beginning and end, preserves silence within the audio, Useful for removing recording artifacts, room tone, or equipment noise, May significantly reduce file duration depending on original content, Preserves original sample rate and audio quality, Be careful with very low thresholds as they may trim wanted quiet content
Inputs: audio_file, threshold_db
Tags: gradio, gradio-mcp-server, audio-processing, mcp-server-track, mcp-server, region:us
Tool: AudioEditor_transcribe_audio_sync
Space: AudioEditor
Description: Synchronously transcribe an audio file using AI-powered speech recognition. This function provides a synchronous wrapper around the async transcription process, converting audio files to text using advanced speech recognition. It handles the async/await complexity internally and returns detailed transcription results including the full text, timestamped segments, language detection, and processing statistics. Returns: A tuple containing four string elements:, status (str): Status message indicating success with language and processing time,, or error information if transcription failed, full_text (str): Complete transcription as plain text, or empty string on error, segments_formatted (str): Formatted text showing timestamped segments with, start/end times and confidence scores, or empty string on error, json_formatted (str): Pretty-formatted JSON string containing complete transcription, data including word-level timestamps and metadata, or empty string on error., , original audio filename, detected language code (e.g., "en", "es", "fr"), complete transcription text, array of text segments with timing and word breakdowns, time taken for transcription, start/end times, text, and words array with individual, word timestamps and confidence scores (0.0-1.0 range), , status, text, segments, json_data = transcribe_audio_sync("url/to/audio.mp3"), , {status}"), {text}"), {segments}"), , {status}"), , Automatically detects language in the audio file, Provides word-level and segment-level timestamps for precise audio editing, Returns confidence scores for quality assessment, Handles various audio formats and sample rates automatically, Processing time depends on audio length and complexity, All timestamps are provided in seconds with decimal precision, Function blocks until transcription is complete (synchronous), For async usage, use process_transcription() directly instead
Inputs: audio_file
Tags: gradio, gradio-mcp-server, audio-processing, mcp-server-track, mcp-server, region:us