alihmaou/Agents_MCP_Hackathon_Tools_List huggingface dataset:
Tool: SmartEnterpriseMCPAgents_mcphawk_get_employee_profile
Space: SmartEnterpriseMCPAgents
Description: Returns the detailed employee profile for a given employee. This tool retrieves the profile details for an individual employee, including role, cloud expertise, data warehouse tools, domain exposure, office location, gender, and other relevant attributes. It queries data from the internal employee master database (`EMPLOYEE_MASTER_DB`) and returns the complete profile as a dictionary. This function is typically used in internal workforce management, reporting dashboards, or talent mapping systems within MCPHawk Inc to retrieve standardized employee data. **Input:** - `employee_name` (str): Full name of the employee (case-insensitive matching recommended) **Returns:** A dictionary containing: - `employee_name`: Name of the employee - `employee_profile`: A dictionary of profile details, such as: { "role": "Sr. DE", "cloud_experties": "AWS", "cloud_native_experties": "dbt", "dw_experties": "Snowflake", "domain_exposure": "Banking & Insurance", "office_location": "Bangalore", "date_of_joining": "2022-08-14", "gender": "Male", "allocation": "Billable", "resume": "resume_amit_sharma.png" } If the employee is not found, a fallback message is returned instead of the profile. **Example Response:** { "employee_name": "Amit Sharma", "employee_profile": { "role": "Sr. DE", "cloud_experties": "AWS", "cloud_native_experties": "dbt", "dw_experties": "Snowflake", "domain_exposure": "Banking & Insurance", "office_location": "Bangalore", "date_of_joining": "2022-08-14", "gender": "Male", "allocation": "Billable", "resume": "resume_amit_sharma.pdf" } }
Inputs: employee_name
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: SmartEnterpriseMCPAgents_mcphawk_get_employee_leave_balance
Space: SmartEnterpriseMCPAgents
Description: Returns the current leave balances for a given employee (India-specific). This tool retrieves leave balance details for an individual employee based in India for the current calendar year. The data includes available days across different leave types (e.g., sick leave, earned leave, paternity leave), and is loaded from the predefined CSV file `sample-data/employee_leave_balances.csv`. Note: This tool is intended only for *India-based employees*. It should not be used for employees located in other countries. HR teams managing India operations or evaluating India leave data can use this to view an employee's current leave status. **Input:** - `employee_name` (str): Full name of the employee (case-insensitive matching recommended when implementing full system) **Returns:** A dictionary containing: - `employee_name`: The name of the employee - `year`: The current year - `leave_balances`: A dictionary of leave types and their available days, e.g.: { "sick_leave": {"available_days": 4, "note": "Refer to policy for applicability."}, "paternity_leave": {"available_days": 10, "note": "Refer to policy for applicability."} } If the employee is not found, a fallback message is returned in place of `leave_balances`. **Example Response:** { "employee_name": "Amit Sharma", "year": 2025, "leave_balances": { "sick_leave": {"available_days": 6, "note": "Refer to policy for applicability."}, "earned_leave": {"available_days": 12, "note": "Refer to policy for applicability."} } }
Inputs: employee_name
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: SmartEnterpriseMCPAgents_mcphawk_suggest_bench_match
Space: SmartEnterpriseMCPAgents
Description: Suggests bench employees (allocation = "On Bench") who match the given employee’s skill profile. **Input:** - `employee_name` (str): Name of the employee to find similar bench profiles for. - `match_level` (str): One of `"strict"`, `"partial"`, or `"none"`: - `"strict"` = Match in all 4 skill dimensions. - `"partial"` = Match in at least 2 of 4 dimensions. - `"none"` = Match in fewer than 2 dimensions. **Returns:** A dictionary with: - `bench_matches`: Dictionary of up to 5 employee profiles who are on bench. **Example Response:** { "bench_matches": { "Neha Kapoor": { "role": "Architect", "cloud_experties": "Azure", "cloud_native_experties": "Fivetran", "dw_experties": "Databricks", "domain_exposure": "Retail", "allocation": "On Bench" }, "Rahul Desai": { "role": "DE", "cloud_experties": "AWS", "cloud_native_experties": "Glue", "dw_experties": "Redshift", "domain_exposure": "Healthcare", "allocation": "On Bench" } } }
Inputs: employee_name
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: SmartEnterpriseMCPAgents_mcphawk_get_project_details
Space: SmartEnterpriseMCPAgents
Description: Returns comprehensive metadata about an MCPHawk Inc projects This tool provides detailed information about a specific project, including: - Project start and end dates - Project classification - 'Tier 1 (High budget, long duration, senior involvement)' - 'Tier 2 (Medium budget and duration)', - 'Tier 3 (Low effort, short-term, often POC or pilot-level) - Impact of the project at customer side to understand what it means to customer and evaluate the risk during execution. - Enablement - Optimization - Innovation - Project complexity to understand and mitigate any project risk during execution and values are - Very High - High - Medium - Simple - Technology detail like - cloud environment (AWS, GCP, Azure) - cloud native tool (dbt, Fivetran, HVR etc) - Data Warehouse Tools (snowflake, databricks, Spark etc) - Project Domain line Marketing, Healthcare, Finance to have a better resource deployment for the succes. - Project Cost in USD to understand the size of the project, so revenue mapping can be done. This tool is useful for project managers, resource planners, and leadership to get a snapshot of project details for risk assessment and future planning **Input:** - `project_name` (str): The name of the project to retrieve details for. **Returns:** A dictionary containing: - `project_name`: The queried project’s name - `start_date`: Project start date (YYYY-MM-DD) - `end_date`: Project end date (YYYY-MM-DD) - `classification`: Project classification level (e.g., Tier 1, Tier 2, Tier 3) - `customer_impact`: Level of impact on customers (e.g., Low, Moderate, High, Innovation, Optimization, Enablement) - `complexity`: Project complexity (e.g., Medium, Very High) - `cloud_environment`: Cloud environment used (e.g., AWS, Azure, GCP, None) - `cloud_native_tool`: Cloud-native tool used (e.g., Fivetran, Matillion, None) - `dw_tool`: Data warehouse or processing tool used (e.g., Databricks, Synapse, Apache Spark) - `project_domain`: Domain or industry focus of the project (e.g., Healthcare, Travel & Hospitality) - `total_cost`: Total project cost in INR (float) If the project is not found, returns a message indicating absence of data. **Example Response:** { "project_name": "Grow Efficient Synergies", "start_date": "2025-02-11", "end_date": "2025-10-08", "classification": "Tier 1 (High budget, long duration, senior involvement)", "customer_impact": "Enablement", "complexity": "Medium", "cloud_environment": "Azure", "cloud_native_tool": "Fivetran", "dw_tool": "Snowflake", "project_domain": "Healthcare", "total_cost": 1018444.0 }
Inputs: project_name
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: SmartEnterpriseMCPAgents_mcphawk_get_project_milestones
Space: SmartEnterpriseMCPAgents
Description: Returns all scheduled milestones and their dates for a given MCPHawk Inc project. This tool delivers a detailed timeline of milestones for a project, including: - Milestone names (e.g., Sprint-1, Sprint-2, QA Release, Production Release) - Corresponding milestone dates - Planned Cumulative Burn in USD for different project milestones - Actual Burn based on MCPHawk Inc employee billing - Cumulative Actual Burn rate basedon Actual Burn rate values (This is important to understand the financials of the project) This tool is helpful for project managers, scrum masters, and stakeholders to track progress, important delivery dates and understand if project is running within budget or not and understnad financial health of the project. **Input:** - `project_name` (str): The project whose milestones are requested. **Returns:** A dictionary containing: - `project_name` (str): The name of the project - `milestones` (list): A list of dictionaries, each with: - `milestone` (str): Name of the milestone (e.g., "Sprint-1", "QA Release") - `milestone_date` (date): Scheduled date for the milestone (in `YYYY-MM-DD` format) - `planned_cumulative_burn` (float): Planned cumulative burn amount by this milestone - `actual_burn` (float): Actual burn recorded for this milestone period - `cumulative_actual_burn` (float): Cumulative total of actual burn up to this milestone If no milestones are found, returns a message indicating absence of data. **Example Response:** { "project_name": "Grow Efficient Synergies", "milestones": [ { "milestone": "Sprint-1", "milestone_date": "2025-02-25", "planned_cumulative_burn": 113160.0, "actual_burn": 106528.85, "cumulative_actual_burn": 106529.0 }, { "milestone": "Sprint-2", "milestone_date": "2025-03-11", "planned_cumulative_burn": 226321.0, "actual_burn": 111444.79, "cumulative_actual_burn": 217974.0 }, { "milestone": "UAT Release", "milestone_date": "2025-05-20", "planned_cumulative_burn": 905283.52, "actual_burn": 107937.74, "cumulative_actual_burn": 881532.64 }, { "milestone": "Production Release", "milestone_date": "2025-05-27", "planned_cumulative_burn": 1018443.96, "actual_burn": 107682.72, "cumulative_actual_burn": 989215.36 } ] }
Inputs: project_name
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: SmartEnterpriseMCPAgents_get_employee_project_assignment
Space: SmartEnterpriseMCPAgents
Description: Returns the mapping of an employee to their project and role within that project for MCPHawk Inc. This tool provides a simple overview of which project an employee is assigned to and their specific role on that project. This tool is useful for team leads, project managers, and HR systems needing to quickly identify employee assignments and responsibilities. **Input:** - `employee_name` (str): The full name of the employee whose project-role mapping is requested. **Returns:** A dictionary containing: - `project_name`: Name of the project the employee is working on - `role`: The employee’s role within that project (e.g., SDE, Architect, DE) If no mapping is found for the given employee, returns a message indicating absence of data. **Example Response:** { "project_name": "Aggregate Innovative Paradigms", "role": "Architect" }
Inputs: employee_name
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: CraftJest_generate_unit_test
Space: CraftJest
Description: Generates a Jest unit test file for the provided source code using the Anthropic API. This function leverages the 'QA-Sentinel' automated test engineering bot to create comprehensive unit tests aiming for 100% statement, branch, function, and line coverage. Returns: The content of the generated Jest test file as a string, or an error message, if the code is not provided or an exception occurs during generation.
Inputs: code, source_code_path, test_root_dir_path, additional_system_prompt, anthropic_api_key
Tags: gradio, mcp-server, region:us
Tool: llmstxt_load_registry
Space: llmstxt
Description: This function (load_registry) should always be called first, before using the load_doc function. It will load the registry file and return the content of the registry. Based on that, you will then be able to pick and chose the documentation you want to load. Returns: The content of the registry, that contains the ID, a description and the URL location for each item of the registry, in JSON format
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: ADE_Explorer_MCP_top_adverse_events_tool
Space: ADE-Explorer-MCP
Description: MCP Tool: Finds the top reported adverse events for a given drug. Returns: A Plotly figure, a Pandas DataFrame, and a summary string.
Inputs: drug_name, top_n, patient_sex, min_age, max_age
Tags: gradio, mcp-server, region:us
Tool: Agent_Geo_run_analysis_pipeline
Space: Agent_Geo
Inputs: collar_file, assay_file, litho_file, litho_dict_file, cm_hole, cm_east, cm_north, cm_elev, cm_dip, cm_azi, am_hole, am_from, am_to, lm_hole, lm_from, lm_to, lm_lith, ldm_code, ldm_desc, primary_element, user_context
Tags: gradio, mcp-server, region:us
Tool: mcp_data_analyst_connect_to_database
Space: mcp-data-analyst
Description: This function is used to connect to a database using the provided database URL and returns its schema. Returns: -------, tuple, , schema (str): A string representation of the database schema if the connection is successful,, or an empty string if it fails., status (str): A message indicating the result of the connection attempt., , -----, The dialect is extracted from the URI to determine the database type (e.g., MySQL, PostgreSQL).
Inputs: db_url
Tags: gradio, mcp-server-track, agent-demo-track, mcp-server, region:us
Tool: mcp_data_analyst_connect_to_file
Space: mcp-data-analyst
Description: This function is used to connect to a data source file like CSV or parquet. Returns: -------, tuple, , schema (str): A string representation of the data schema if successful, or an empty string on failure., status (str): A message indicating the success or failure of the connection.
Inputs: file
Tags: gradio, mcp-server-track, agent-demo-track, mcp-server, region:us
Tool: mcp_data_analyst_generate_and_run_sql
Space: mcp-data-analyst
Description: This function is used to give an answer to a user question related to a database or file source. Returns: -------, str, The given response of the user question, , -----, Requires a connected `source` (via `connect_to_file` or `connect_to_database`)., Uses a language agent to interpret the prompt and generate an appropriate SQL query or explanation.
Inputs: prompt
Tags: gradio, mcp-server-track, agent-demo-track, mcp-server, region:us
Tool: get_position
Space: chess-mcp-server
Description: Describe the current chess position from a FEN string, plus a material summary. Attempts to classify the opening, and if successful, adds the opening information to the position. Otherwise, it adds a piece map with the current pieces and the list of legal moves.
Inputs: fen
Tags: docker, mcp-server, mcp-server-track, chess, chessboard, games, region:us
Tool: get_top_moves
Space: chess-mcp-server
Description: Get the top N moves for a given chess position using StockFish. Returns a list of the top moves with their absolute scores (in centipawns) and whether they are leading to a mate. DISCLAIMER: This function uses the Stockfish chess engine, ONLY use it if explicitly allowed.
Inputs: fen, top_n
Tags: docker, mcp-server, mcp-server-track, chess, chessboard, games, region:us
Tool: analyze_tactical_patterns
Space: chess-mcp-server
Description: Analyze immediate tactical patterns from a given FEN string. This function detects: - Potential knight forks and double attacks (refering to next move) - Pins, skewers, discovered attacks and x‐ray attacks in the current position.
Inputs: fen
Tags: docker, mcp-server, mcp-server-track, chess, chessboard, games, region:us
Tool: find_opening_by_name
Space: chess-mcp-server
Description: Search for a chess opening by its name in the Lichess openings database. Return the ECO code, name, PGN, FEN and sub-variations of a chess opening by its name. The name is matched case-insensitively.
Inputs: name
Tags: docker, mcp-server, mcp-server-track, chess, chessboard, games, region:us
Tool: run_my_script_run_script
Space: run_my_script
Description: Executes a user-provided self contained Python script inside an isolated virtual environment with automatic dependency management. This function is intended to serve as a backend execution engine in a Model Context Protocol (MCP) server setting, where a language model may submit scripts for evaluation. It creates a secure workspace, detects dependencies, installs them using `uv`, executes the script, captures its output (including stdout and generated files), and returns all relevant results. ⚠️ Limitations & Guidance for LLM-based Use: - Scripts should be self-contained, avoid system-level access, and primarily focus on data processing, text generation, visualization, or machine learning tasks. - The code can output logs, JSON, images, CSVs, or any other files, which are returned as artifacts. - Avoid infinite loops or long-running background processes. Timeout support can be added externally. Returns: , logs (str): Full stdout and stderr logs of the executed script., artifacts (Dict[str, str]): A dictionary of output files with their names and summaries or indicators, (e.g., image or CSV placeholders). Includes a "__workdir__" key pointing to the working directory., zip_path (str): Path to a ZIP archive containing all output artifacts for download.
Inputs: code, user_input, cleanup_enabled, cleanup_after
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: ShallowCodeResearch_agent_research_request
Space: ShallowCodeResearch
Description: This function researches a coding request from the user, generates code, executes it, and returns a clean summary of the results. This is an mcp server function that responds to research coding requests from users. Returns: A tuple containing the JSON result from the orchestrator and a clean summary
Inputs: user_request
Tags: gradio, mcp, multi-agent, research, code-generation, ai-assistant, python, web-search, llm, modal, mcp-server-track, mcp-server, region:us
Tool: ShallowCodeResearch_agent_question_enhancer
Space: ShallowCodeResearch
Description: Wrapper for QuestionEnhancerAgent to provide question enhancement. Returns: Enhanced question result with sub-questions
Inputs: user_request
Tags: gradio, mcp, multi-agent, research, code-generation, ai-assistant, python, web-search, llm, modal, mcp-server-track, mcp-server, region:us
Tool: ShallowCodeResearch_agent_web_search
Space: ShallowCodeResearch
Description: Wrapper for WebSearchAgent to perform web searches. Returns: Web search results with summaries and URLs
Inputs: query
Tags: gradio, mcp, multi-agent, research, code-generation, ai-assistant, python, web-search, llm, modal, mcp-server-track, mcp-server, region:us
Tool: ShallowCodeResearch_agent_llm_processor
Space: ShallowCodeResearch
Description: Wrapper for LLMProcessorAgent to process text with LLM. Returns: LLM processing result with output and metadata
Inputs: text_input, task, context
Tags: gradio, mcp, multi-agent, research, code-generation, ai-assistant, python, web-search, llm, modal, mcp-server-track, mcp-server, region:us
Tool: ShallowCodeResearch_agent_citation_formatter
Space: ShallowCodeResearch
Description: Wrapper for CitationFormatterAgent to format citations. Returns: Formatted citations result with APA-style references
Inputs: text_block
Tags: gradio, mcp, multi-agent, research, code-generation, ai-assistant, python, web-search, llm, modal, mcp-server-track, mcp-server, region:us
Tool: ShallowCodeResearch_agent_code_generator
Space: ShallowCodeResearch
Description: Wrapper for CodeGeneratorAgent to generate Python code. Returns: A tuple containing the generation result and raw code
Inputs: user_request, grounded_context
Tags: gradio, mcp, multi-agent, research, code-generation, ai-assistant, python, web-search, llm, modal, mcp-server-track, mcp-server, region:us
Tool: ShallowCodeResearch_code_runner_wrapper
Space: ShallowCodeResearch
Description: Wrapper for CodeRunnerAgent that uses async execution with warm pool. Ensures a sandbox is spawned if not already present, waits for readiness, and then executes the code. Provides user-friendly error messages. Returns: The execution result or user-friendly error message
Inputs: code_or_obj
Tags: gradio, mcp, multi-agent, research, code-generation, ai-assistant, python, web-search, llm, modal, mcp-server-track, mcp-server, region:us
Tool: ShallowCodeResearch_get_health_status
Space: ShallowCodeResearch
Description: Get comprehensive system health status including advanced monitoring features. Retrieves detailed health information about the system including availability of advanced features, system resources, and operational metrics. Returns basic information if advanced monitoring is not available. Returns: A dictionary containing system health status and metrics
Tags: gradio, mcp, multi-agent, research, code-generation, ai-assistant, python, web-search, llm, modal, mcp-server-track, mcp-server, region:us
Tool: ShallowCodeResearch_get_performance_metrics
Space: ShallowCodeResearch
Description: Get performance metrics and analytics for the MCP Hub system. Collects and returns performance metrics including execution times, success rates, error counts, and resource utilization. Provides basic information if advanced metrics collection is not available. Returns: A dictionary containing performance metrics and statistics
Tags: gradio, mcp, multi-agent, research, code-generation, ai-assistant, python, web-search, llm, modal, mcp-server-track, mcp-server, region:us
Tool: timeline_mcp_hackathon_extract_key_info_prompt
Space: timeline_mcp_hackathon
Description: Generate a perfect prompt for extraction of key information from text and formatting of the output as JSON that will enable building a timeline of events. Use this tool with keyword arguments: extract_key_info_prompt(text="..."). Returns: Array of chat messages to be sent to a language model for information extraction.
Inputs: text
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: timeline_mcp_hackathon_build_timeline_prompt
Space: timeline_mcp_hackathon
Description: Generate a user-friendly, chronologically ordered timeline from a list of structured events. Use this tool with keyword arguments: build_timeline_prompt(events=[]). Returns: A sequence of chat messages forming a prompt for an LLM to generate a readable timeline.
Inputs: events
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: egg_tart_agent_ask
Space: egg_tart_agent
Description: This function serves as the Model Control Panel (MCP) API endpoint for the Egg Tart agent, which is designed to answer questions about Hong Kong using over 1,800 datasets from data.gov.hk. She provides concise, friendly, and markdown-formatted answers to a wide range of queries, including weather, transportation,demographics, public holidays, and economic statistics. Returns: A concise, markdown-formatted answer generated by the agent, including relevant data and citations.
Inputs: question
Tags: gradio, mcp-server-track, agent-demo-track, mcp-server, region:us
Tool: T2I_text_to_images_and_base64_generator
Space: T2I
Description: Converts text into images, returning both PIL objects for UI preview and base64 strings for an API/MCP client, with customizable options.
Inputs: text_content, style, font_size, bg_color_name, aspect_ratio_str, font_choice
Tags: gradio, mcp-server-track, agent-demo-track, text-to-image, document-generation, image-creation, text-formatting, visual-content, mcp-server, region:us
Tool: mcp_drawittome_generateDiagram
Space: mcp_drawittome
Description: Generates a diagram to explain a submitted text. The diagram is returned in html  with this pattern <a href="https://path/diagram.svg" target="_blank"> <img src="https://path/diagram.svg" alt="Diagram image" > </a>. It is not needed to handle the code, just present the html result to the user. It handles various types like Graph TB (default, block grouping with colors), flowchart TD (decision processes), sequenceDiagram (actor/system interactions), classDiagram (technical models like SQL schemas), stateDiagram-v2 (state logic or user stories), erDiagram (data entity relationships), gantt (projects and task timelines along dates), and timeline (historical/biographical sequences grouped by decade or century). Returns: html diagram ready to display made with this pattern <a href="https://path/diagram.svg" target="_blank"> <img src="https://path/diagram.svg" alt="Diagram image" > </a>
Inputs: input_text, input_precisions
Tags: gradio, mcp-server-track, sambanova, mcp-server, region:us
Tool: FluxFoundry_deploy_for_user
Space: FluxFoundry
Description: Deploy a Modal endpoint for a user using their API key. Unless the user has already done this, this must be done before they can train a LoRA. This function deploys a Modal script using the user's Modal API token ID and secret, allowing them to set up their own training endpoint. You can create them at https://modal.com/settings/tokens. Returns: tuple[str, str, str]: (status_message, stdout, stderr), status_message: Human-readable deployment status, stdout: Standard output from the modal deploy command, stderr: Standard error output from the modal deploy command, , status, out, err = deploy_for_user("ak-1234567890abcdef", "as-secret123...")
Inputs: token_id, token_secret
Tags: gradio, mcp-server-track, modal, mcp-server, region:us
Tool: FluxFoundry_check_model_access
Space: FluxFoundry
Description: Check if the user has access to the gated FLUX.1-dev model. This function verifies that the user's HuggingFace token has access to the gated FLUX.1-dev model required for LoRA training. This has to be done before we can deploy the endpoint. Returns: str: Status message indicating access status and next steps, , status = check_model_access("hf_abcdef123456")
Inputs: hf_token
Tags: gradio, mcp-server-track, modal, mcp-server, region:us
Tool: FluxFoundry_start_training
Space: FluxFoundry
Description: Start a LoRA training job for Flux image generation model. This function initiates a LoRA (Low-Rank Adaptation) training job on a dataset of images. It sends a request to a Modal API endpoint to start the training process. Returns: tuple[str, str]: (status_message, job_id), status_message: Human-readable status with training details or error message, job_id: Unique identifier for the training job, empty string if failed, , status, job_id = start_training(, dataset_id="myuser/dog-photos",, hf_token="hf_abcdef123456",, output_repo="myuser/my-dog-lora",, instance_name="Fluffy",, class_name="dog",, max_train_steps=500, )
Inputs: dataset_id, hf_token, output_repo, start_training_url, instance_name, class_name, max_train_steps
Tags: gradio, mcp-server-track, modal, mcp-server, region:us
Tool: FluxFoundry_check_job_status
Space: FluxFoundry
Description: Check the current status of a LoRA training job or image generation job. This function queries the Modal API to get the current status of a training job using its unique job ID. It returns detailed information about the job progress. Note that if we are invoking this function with MCP, the user cannot neccecarily see the images in the tool call, so you will have to render them again in the chat. **MCP Client Limitation:** Due to MCP client constraints, we cannot render a gallery of images in the chat. The MCP client should render these URLs as clickable markdown links when possible. Returns: tuple[str, List[Image.Image]]: (status_message, all_images), status_message: Detailed status message containing job information, all_images: List of PIL Image objects if images are available, empty list otherwise, , "completed": Job finished successfully, "running": Job is still in progress, "failed": Job failed due to an error, "error": System error occurred, , //modal-app-url-api-job-status.modal.run")
Inputs: job_id, job_status_url
Tags: gradio, mcp-server-track, modal, mcp-server, region:us
Tool: FluxFoundry_generate_images
Space: FluxFoundry
Description: Generate images using a trained LoRA model. This function sends a request to generate images using a previously trained LoRA model. It takes a list of prompts and generates images for each one. Returns: tuple[str, str]: (status_message, job_id), status_message: Human-readable status with generation details or error message, job_id: Unique identifier for the generation job, empty string if failed, , status, job_id = generate_images(, prompts_json='["a photo of a dog", "a photo of a cat"]',, lora_repo="myuser/my-dog-lora",, hf_token="hf_abcdef123456",, //modal-app-url-api-generate-images.modal.run", )
Inputs: prompts_json, lora_repo, hf_token, generate_images_url
Tags: gradio, mcp-server-track, modal, mcp-server, region:us
Tool: MistyClimateServer_process_climate_document
Space: MistyClimateServer
Description: The function `process_climate_document` extracts climate data from either a file or URL input and returns structured JSON data. :param api_key: The `api_key` parameter is typically a unique identifier or access token that allows you to authenticate and access a specific API or service. It is used in the `process_climate_document` function to authenticate and make requests to the `extract_climate_data` function. You need to provide a valid :param file: The `file` parameter in the `process_climate_document` function is used to pass a file object containing climate document data. If this parameter is provided, the function will extract climate data from the file using the `extract_climate_data` function :param url_input: The `url_input` parameter in the `process_climate_document` function is used to provide a URL input for extracting climate data. This URL should point to a document or webpage containing climate-related information that needs to be analyzed. The function will extract data from this URL if it is provided :return: The function `process_climate_document` returns a JSON object containing the analysis results of a climate document including climate_data, chart_descriptions, and extracted_text.
Inputs: api_key, file, url_input
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: MistyClimateServer_analyze_image
Space: MistyClimateServer
Description: The function `analyze_image` takes an image, analyzes it using a chat model, and returns JSON output with information about the image content. :param api_key: The `api_key` parameter is a string that represents the API key required for authentication to access the chat API service. This key is used to initialize the client for making requests to the service :param image: The `analyze_image` function you provided seems to be a Python function that takes an API key and an image as input parameters. The function is designed to analyze the image using a chat completion model and provide a JSON output with specific fields related to the image content :return: The `analyze_image` function returns a JSON string containing information about the analyzed image. The JSON output includes fields such as image_type, climate_feature, location, date_captured, cloud_density, temperature_anomaly, and description. If the image analysis is successful, the function returns the analyzed results in JSON format. If there is an error during processing, it returns an error message with default values for
Inputs: api_key, image
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: NewsShots_text_to_audio_interface
Space: NewsShots
Description: Convert a short piece of text to speech using an AI text-to-audio model. Provide all the news in one call instead of making multiple separate calls. This helps improve the quality and flow of the speech output. Returns: , str: A JSON-formatted string with metadata about the generated audio., str | None: Filepath to the generated audio file (MP3), or None if generation failed.
Inputs: text, use_music, auto_play
Tags: gradio, mcp-server-track, Modal, mcp-server, region:us
Tool: MCPyLate_pylate_search_leetcode
Space: MCPyLate
Description: Search the PyLate with multi-vector models in the leetcode collection containing code problems solutions and return top-k hits. Returns: List of search results with docid, score, text snippet, and index name
Inputs: query
Tags: gradio, ColBERT, PyLate, mcp-server-track, mcp-server, region:us
Tool: BioMedNorm_MCP_Server_extract_and_normalize
Space: BioMedNorm-MCP-Server
Description: Extract entities from a paragraph and normalize them in one operation. Returns: List of best matching standard terms in the same order as the, input terms. An entry may be None if normalization failed.
Inputs: paragraph, target_entity
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: GPXRunnerTools_mcp_extract_basic_metrics
Space: GPXRunnerTools
Description: Extracts key metrics from a running activity stored in a GPX file. Returns: A dictionary containing:, distance_km (float): Total distance in kilometers, duration (str): Total time in HH:MM:SS, duration_seconds (int): Duration in seconds, average_pace_min_per_km (float): Average pace in min/km, elevation_gain (int): Total positive elevation gain in meters, average_hr (int or None): Average heart rate, if available, max_hr (int or None): Maximum heart rate, if available
Inputs: file
Tags: gradio, mcp-server, region:us
Tool: GPXRunnerTools_mcp_analyze_hr_zones
Space: GPXRunnerTools
Description: Analyze time spent in heart rate zones from GPX data. Returns: A dictionary containing:, time_in_zones (dict): Mapping of zone name to time in seconds, total_tracked_seconds (int): Total duration with HR data, method (str): The zoning method used
Inputs: file, hr_max, hr_rest, method
Tags: gradio, mcp-server, region:us
Tool: GPXRunnerTools_mcp_detect_intervals
Space: GPXRunnerTools
Description: Detects effort and recovery intervals using pace-based z-score logic. Returns: A dictionary containing:, intervals (list): List of detected intervals (type, start, end, duration, avg pace), method (str): Detection method used, baseline_pace_min_per_km (float): Median baseline pace used, total_effort_blocks (int): Number of detected effort intervals, total_blocks (int): Total intervals (effort + recovery), expected_repetitions (int): Provided expected number, if any, match_expectation (bool): Whether the number of efforts matches the expected count
Inputs: file, mode, threshold_ratio, min_effort, min_recovery, window, expected_reps
Tags: gradio, mcp-server, region:us
Tool: GPXRunnerTools_mcp_convert_pace
Space: GPXRunnerTools
Description: Converts a running pace between min/km and min/mile. Returns: A dictionary containing:, original_value (float): The original input pace, converted_value (float): The converted pace value, input_unit (str): The original pace unit, output_unit (str): The target pace unit
Inputs: value, input_unit, output_unit
Tags: gradio, mcp-server, region:us
Tool: podcastify_generate_podcast
Space: podcastify
Description: Generate a podcast-style conversation from various input types. This function takes content from a document URL, webpage link, or raw text and converts it into a natural-sounding podcast dialogue between two hosts. The conversation is then synthesized into audio using text-to-speech. Returns: Path to the generated audio file (.wav format) containing the synthesized, podcast conversation., , If the input text cannot be extracted or is empty, If conversation extraction fails after maximum retries
Inputs: input_type, input
Tags: gradio, Agents-MCP-Hackathon, mcp-server-track, mcp-server, region:us
Tool: MCP_Server_Odoo_ERP_predict_modal_lead
Space: MCP-Server-Odoo-ERP
Description: Prédiction Modal pour un lead
Inputs: name, industry, company_size, budget_range, urgency, source, expected_revenue, response_time
Tags: gradio, mcp-server-track, odoo, ai, crm, sales, mcp-server, region:us
Tool: ai_image_video_mcp_server_upscale_image_preferred
Space: ai-image-video-mcp-server
Description: Edit a local image file using the 'Tile Upscaler' method. This is the preferred upscale method. Optionally save the result. Returns: The upscaled image (PIL.Image) generated using ControlNet + RealESRGAN.
Inputs: path, output_path, resolution, steps, strength, hdr, guidance
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: ai_image_video_mcp_server_upscale_image
Space: ai-image-video-mcp-server
Description: Edit a local image using the 'Clarity Upscaler' method. Optionally save result. Useful for stylized upscaling with fractal detail control. Returns: The edited image (PIL.Image) upscaled via AI model.
Inputs: path, output_path, scale, dynamic, creativity, resemblance, tiling_width, tiling_height, model, scheduler, steps, seed, downscale, downscale_resolution
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: ai_image_video_mcp_server_generate_video_from_image
Space: ai-image-video-mcp-server
Description: Generate a video from an image and a prompt using the Google Veo-2.0 model. Returns: Path to the generated video file (temporary file used for Gradio display).
Inputs: path, prompt, aspect_ratio, duration, output_path
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: ai_image_video_mcp_server_edit_image_with_gemini
Space: ai-image-video-mcp-server
Description: Edits an image using Gemini 2.0 Flash Preview Image Generation by applying a prompt to a reference image. This is typically used to generate a background scene behind a subject (e.g., a person or object with background removed), in preparation for video generation. The prompt should clearly describe the desired environment or context **without altering the subject itself**. For example: "Place this car in the desert of Mars, but do not change the car.". Returns: Path to the generated image (temporary file used for Gradio display or further processing).
Inputs: path, prompt, output_path
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: gradio_themerconvert_css_to_theme
Space: gradio-themer
Description: Convert CSS styles or style descriptions into standardized theme JSON format using HF hosted LLM.
Inputs: css_input, theme_name, user_token, model_choice
Tags: gradio, mcp-server-track, custom-component-track, Agents-MCP-Hackathon, theming, agent-ui, mcp-server, region:us
Tool: Patent_Scout_analyze_patent_idea
Space: Patent-Scout
Description: Analyzes a product idea to assess its patentability and finds similar patents. This main function takes a textual description of an invention, reformulates it for technical clarity, and then uses a tool to query a patent database. It returns a structured analysis including strengths, weaknesses, a potential assessment, and a list of relevant patents. Returns: The final structured result is embedded as JSON in the last status message within an HTML comment., {...} -->" in the final yielded status to extract the complete analysis result., strengths (list), weaknesses (list), rating (dict), patents (list), rewritten_query (str), status (str)., Complete analysis results for all UI components.
Inputs: query
Tags: gradio, mcp-server-track, agent-demo-track, mcp-server, region:us
Tool: Stack_Overflow_MCP_Server_search_by_error_sync
Space: Stack_Overflow_MCP_Server
Description: Search Stack Overflow for solutions to an error message. Returns: Formatted search results
Inputs: error_message, language, technologies, min_score, has_accepted_answer, limit, response_format, api_key
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: Stack_Overflow_MCP_Server_analyze_stack_trace_sync
Space: Stack_Overflow_MCP_Server
Description: Analyze a stack trace and find relevant solutions on Stack Overflow. Returns: Formatted search results
Inputs: stack_trace, language, min_score, has_accepted_answer, limit, response_format, api_key
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: Stack_Overflow_MCP_Server_advanced_search_sync
Space: Stack_Overflow_MCP_Server
Description: Advanced search for Stack Overflow questions with comprehensive filters. Returns: Formatted search results
Inputs: query, tags, excluded_tags, min_score, title, body, min_answers, has_accepted_answer, min_views, sort_by, limit, response_format, api_key
Tags: gradio, mcp-server-track, mcp-server, region:us
Tool: etheroi_generate_all_content
Space: etheroi
Description: Generate content for all platforms using Modal AI or local service
Inputs: business_type, target_audience, content_goal, brand_name, key_message, use_modal, model_name
Tags: gradio, custom-component-track, agent-demo-track, ai-agents, content-creation, social-media, modal, marketing, business, mcp-server, region:us
Tool: veterinary_dicom_mcp_enhance_dicom_image
Space: veterinary-dicom-mcp
Description: Verbessert veterinärmedizinische DICOM-Bilder mit verschiedenen Algorithmen.
Inputs: image_file, species, enhancement_method, body_part
Tags: gradio, mcp-server-track, veterinary, medical-imaging, hackathon-2025, mcp-server, region:us
Tool: veterinary_dicom_mcp_compare_enhancement_methods
Space: veterinary-dicom-mcp
Description: Vergleicht verschiedene Bildverbesserungsmethoden für veterinärmedizinische Analyse.
Inputs: image_file, species, body_part
Tags: gradio, mcp-server-track, veterinary, medical-imaging, hackathon-2025, mcp-server, region:us
Tool: TS_Analysis_handle_mcp_analyze
Space: TS_Analysis
Description: Función auxiliar para interfaz Gradio. Convierte strings JSON a listas y llama mcp_analyze_trends. Parámetros: - data_str (str): JSON string con array de números - dates_str (str): JSON string con array de fechas (opcional) - window (int): Ventana de tendencias - method (str): Método de detección de anomalías Retorna resultado de mcp_analyze_trends o mensaje de error.
Inputs: data_str, dates_str, window, method
Tags: gradio, mcp-server, region:us
Tool: TS_Analysis_mcp_get_analysis_report
Space: TS_Analysis
Description: Recupera el reporte textual completo de un análisis previo a partir de su ID. Parámetros: - analysis_id (str): Identificador único del análisis almacenado, obtenido de mcp_analyze_trends. Formato: "mcp_analysis_YYYYMMDD_HHMMSS" Retorna: - str: Reporte completo en formato Markdown con: * Estadísticas básicas (media, desviación, min/max) * Análisis de tendencias (fuerza y dirección) * Detección de anomalías (cantidad y porcentaje) * Análisis de estacionalidad (patrones detectados) * Picos y valles (conteo de extremos) * Información de pronóstico si está disponible En caso de error o ID no encontrado, devuelve mensaje de error.
Inputs: analysis_id
Tags: gradio, mcp-server, region:us
Tool: TS_Analysis_handle_mcp_forecast
Space: TS_Analysis
Description: Función auxiliar para interfaz Gradio. Convierte string JSON a lista y llama mcp_forecast_series. Parámetros: - data_str (str): JSON string con array de números históricos - periods (int): Períodos a pronosticar Retorna resultado de mcp_forecast_series o mensaje de error.
Inputs: data_str, periods
Tags: gradio, mcp-server, region:us
Tool: Statistical_Analysis_MCP_student_t_test
Space: Statistical-Analysis-MCP
Description: Accepts two groups of numeric data as comma-separated strings or DataFrame columns and performs Student's t-test. This test determines whether two independent groups have significantly different means, assuming populations from which the groups were sampled have equal variances (if this assumption is violated, or if equal population variance cannot be verified, use Welch's t-test instead). The test calculates a t-statistic quantifying the mean difference as a multiple of pooled standard deviation. Given an alternative hypothesis (group1 ≠ group2, group1 < group2, or group1 > group2), it outputs the p-value: the probability of observing this result (or more extreme) if no true difference exists. Results are statistically significant when p-value < alpha (typically 0.05). Cohen's d measures practical effect size, standardized by pooled standard deviation, with interpretation: |d| < 0.2 = negligible, 0.2-0.5 = small, 0.5-0.8 = medium, >0.8 = large (custom thresholds may be used). EXAMPLE USE CASES: treatment vs control groups, before/after measurements with different participants, comparing performance between demographic groups.
Inputs: group1_str, group2_str, alternative, alpha, effect_thresholds
Tags: gradio, mcp-server, region:us
Tool: Statistical_Analysis_MCP_welch_t_test
Space: Statistical-Analysis-MCP
Description: Accepts two groups of numeric data as comma-separated strings or DataFrame columns and performs Welch's t-test. This test determines whether two independent groups have significantly different means. This test is valid even when populations have different variances. Default to this test instead of students t-test if you are unsure about population variance. This test calculates a t-statistic using Welch's formula that accounts for unequal variances. Given an alternative hypothesis (group1 ≠ group2, group1 < group2, or group1 > group2), it outputs the p-value: the probability of observing this result (or more extreme) if no true difference exists. Results are considered statistically significant when p-value < alpha (typically 0.05). Cohen's d measures practical effect size, calculated using pooled standard deviation for consistency with other t-tests, with interpretation: |d| < 0.2 = negligible, 0.2-0.5 = small, 0.5-0.8 = medium, >0.8 = large (custom thresholds may be used). EXAMPLE USE CASES: treatment vs control groups, before/after measurements with different participants, comparing performance between demographic groups.
Inputs: group1_str, group2_str, alternative, alpha, effect_thresholds
Tags: gradio, mcp-server, region:us
Tool: Statistical_Analysis_MCP_paired_t_test
Space: Statistical-Analysis-MCP
Description: Accepts two groups of paired numeric data as comma-separated strings or DataFrame columns and performs a paired samples t-test. This test determines whether there is a significant difference between two related measurements (same subjects measured twice), such as before/after treatment measurements. Unlike independent samples t-tests, this test accounts for the correlation between paired observations, making it more powerful for detecting differences in repeated measures designs. The test calculates a t-statistic based on the mean of the differences between paired observations. Given an alternative hypothesis (group1 ≠ group2, group1 < group2, or group1 > group2), it outputs the p-value: the probability of observing this result (or more extreme) if no true difference exists. Results are statistically significant when p-value < alpha (typically 0.05). Cohen's d measures practical effect size, calculated as the mean difference divided by the standard deviation of differences, with interpretation: |d| < 0.2 = negligible, 0.2-0.5 = small, 0.5-0.8 = medium, >0.8 = large (custom thresholds may be used). EXAMPLE USE CASES: before/after treatment measurements on same subjects, pre/post test scores, repeated measurements over time.
Inputs: group1_str, group2_str, alternative, alpha, effect_thresholds
Tags: gradio, mcp-server, region:us
Tool: Statistical_Analysis_MCP_one_sample_t_test
Space: Statistical-Analysis-MCP
Description: Accepts a single group of numeric data as comma-separated string or DataFrame column and performs a one-sample t-test against a known or hypothesized population mean. This test determines whether the sample mean differs significantly from the specified population mean. The test calculates a t-statistic quantifying how many standard errors the sample mean is away from the hypothesized population mean. Given an alternative hypothesis (sample ≠ population, sample < population, or sample > population), it outputs the p-value: the probability of observing this result (or more extreme) if the true population mean equals the hypothesized value. Results are statistically significant when p-value < alpha (typically 0.05). Cohen's d measures practical effect size, calculated as the difference between sample and population means divided by the sample standard deviation, with interpretation: |d| < 0.2 = negligible, 0.2-0.5 = small, 0.5-0.8 = medium, >0.8 = large (custom thresholds may be used). EXAMPLE USE CASES: testing if sample mean differs from known standard, quality control against specification, comparing sample performance against established benchmark.
Inputs: group_str, population_mean, alternative, alpha, effect_thresholds
Tags: gradio, mcp-server, region:us