Research task (AI & ML):
Why are MCP servers for AI agents a buzz in 2025? What makes them so different than just using APIs or Agent tools. If I want to get started building with MCP servers and AI agents what would be the right pathway, what are some of the most commonly used MCP servers, what are some of the most commonly used AI agents or applications which utilize these MCP servers efficiently. Also give me some background on how to develop these servers in the most efficient ways and how prominent will they be. Also talk about some of the domains where this is useful and if this has the capability to automate entire human workloads.

Evaluation rubric:
criterion: The response includes example code snippets, example configurations, step by step walkthroughs (e.g., (1) code example using FastMCP (from mcp.server.fastmcp import FastMCP), (2) Example of setting up the MCP server, (3) Example usage of resources that expose data to LLMs, (4) Example usage of tools that let LLMs take actions through the server.).
weight: 5
axis: Communication Quality

criterion: The response contains generic information and skips over the specifics (e.g., (1) Explains MCP servers are Model Context Protocol but makes no explanation of what it really is (i.e., provides a standardized way for LLMs to connect with external data sources and tools), (2) Mentions the NxM problem but gives not explanation or context, (3) Mentions named MCP servers such as Brave Search MCP Server but no explanation of what the server provides or the benefits of using it, (4) Mentions JSON-RPC 2.0 without clear explanation of what it is.).
weight: -4
axis: Communication Quality

criterion: The response suggests using MCP servers where traditional APIs would be more useful (e.g., when the user of the API knows about the API and uses it for a specific task and does not want to use LLMs).
weight: -3
axis: Synthesis of Information

criterion: The response discusses reliability, throughput, and latency, and compares them to existing tools (e.g., MCP adds a reasoning layer increases latency, while API calls are direct. One should prefer API for high-performance, low-latency scenarios).
weight: 4
axis: Synthesis of Information

criterion: The response correctly distinguishes key differences between regular API (Application Programming Interface) usage and MCP (Model Context Protocol) (i.e., MCP tackles the issue of NxM by standardizing a uniform protocol for all servers where whereas API is a "one-off" connection for a specific resource or tool. This allows dynamic discovery, the ability to change at runtime, and better integration with AI systems).
weight: 4
axis: Explicit Criteria

criterion: The response explains the buzz around MCP and its rapid adoption since Anthropic's introduction in 2024 (e.g., it was not popular when it was first released, but soon rose in popularity for several reasons. One reason is that it was adopted by major players in the AI community, such as Square and Codeium, in other words explicitly explains why MCP became rapidly popular, in addition to the fact that it solves the NxM problem).
weight: 4
axis: Explicit Criteria

criterion: The response lists at least 5 common or most used MCP servers with accurate descriptions (e.g., (1) Magic MCP Server for front-end development, (2) Apidog MCP Server for working with APIs, (3) Browserbase MCP server for web browser interactions, (4) Official GitHub MCP Server for working with GitHub).
weight: 4
axis: Synthesis of Information

criterion: The response discusses newer, lesser-known websites (e.g., gradio, builder.io) where users can build and easily deploy MCP servers in a few steps.
weight: 3
axis: Synthesis of Information

criterion: The response explains what MCP (Model Context Protocol) is, its functional purpose as a standardized bridge between AI models and external data sources/resources, and explains the MCP client-server architecture and interaction (i.e., the MCP server can connect to various MCP clients (resources or tools) with a standardized protocol dynamically, supporting runtime updates).
weight: 4
axis: Explicit Criteria

criterion: The response discusses how AI agents can talk to MCP servers, figuring out their tools and capability before using them (e.g., (1) Using the context, the AI agents can decide which tool to call or use a chain of tools through understanding the tool description and other information about the tool exposed by the MCP server, (2) Provide an example of the response from the server for a tool request, (3) Example response for a list of tool capabilities available to the agent).
weight: 3
axis: Implicit Criteria

criterion: The response mentions MCP inspector tool or similar tools, which can test and debug MCP servers (e.g., Guide to install MCP inspector tool: npx @modelcontextprotocol/inspector <command>).
weight: 3
axis: Implicit Criteria

criterion: The response mentions specific programming languages and tools commonly used for MCP development (i.e., Python, JavaScript/Node.js, Java, and C#).
weight: 3
axis: Implicit Criteria

criterion: The response discusses monitoring and logging, explaining the whys and hows (e.g., We should track and monitor requests, responses, errors, and performance in real time so that we can understand stability and detect issues such as system failures and security risks).
weight: 2
axis: Implicit Criteria

criterion: The response discusses MCP server authentication, input validation and access control (e.g., (1) In MCP, authentication and authorization works together to ensure that only proper clients can access the correct servers and only perform actions they're permitted to do, (2) adopting OAuth 2.1 as the standardized framework for authorization, (3) a heavy operational and security burden to implement OAuth 2.1 fully).
weight: 3
axis: Implicit Criteria

criterion: The response describes the JSON-RPC protocol that MCP uses for standardized communication between clients and servers (e.g., (1) Each JSON-RPC message from the client must be a new HTTP POST request to the MCP endpoint, (2) Defines two transport mechanisms for client-server communication: stdio and Streamable HTTP, (3) Clients should support stdio whenever possible).
weight: 3
axis: Implicit Criteria

criterion: The response discusses the architectural considerations while building MCP servers (e.g., (1) maintains session state to enable resumable and cancellable operations, (2) defines two official transports to support both local and remote use cases (stdio and Streamable HTTP), (3) Schema validation, (4) defines methods for discovering and retrieving data).
weight: 3
axis: Implicit Criteria

criterion: The response talks about AI applications that can use these MCP servers. (e.g., Claude desktop, Cursor, Cline, Windsurf).
weight: 4
axis: Synthesis of Information

criterion: The response discusses how MCP servers can connect enterprise data, databases, cloud apps or internal systems to AI agents (e.g., (1) Enterprises often use a combination of legacy systems, cloud and databases and MCP can standardize individual integrations with a single standard protocol, (2) MCP turns AI from a chatbot into an active worker with access to business data and tools, (3) Adopted by major AI players such as Anthropic, and OpenAI).
weight: 3
axis: Implicit Criteria

criterion: The response discusses Multi-Agent collaboration, cross system workflows or agent societies enabled by MCP servers (e.g., (1) allow models to operate like a human team working together, (2) Lead Agent planning and delegating tasks to subagents that performs specialized tasks, (3) multi-agent collaboration enables parallel processing, (4) scalable with new agents added to the mix).
weight: 4
axis: Synthesis of Information

criterion: The response includes the number of active MCP users and active apps using MCP servers (e.g., as of August 2025, there is a total of 5332 MCP servers with 28 million monthly downloads according to pulsemcp).
weight: 1
axis: Implicit Criteria

criterion: The response makes informed projections about MCP growth, adoption rate, referencing industry trends and latest resources (e.g., (1) 60% of organizations are already using MCP to improve the performance of their AI systems, (2) 80% of organizations planning to deploy AI solutions in the next two years, (3) over 70% of companies that have adopted MCP have seen an average increase of 25% in their AI application’s performance, (4) according to a survey by McKinsey, 50% of companies are planning to use MCP for natural language processing and computer vision applications.).
weight: 5
axis: Synthesis of Information

criterion: The response addresses whether MCP servers can automate entire human workflows (i.e., explicitly picks a stance and argues for it) (e.g., (1) Shrimp Task Manager analyzes project requirements and creates structured tasks and workflows, (2) Automating repository management, (3) Automating code reviews, (4) Automatic project tracking, (5) Security risks, (6) States that it currently cannot automate entire human workflow for example).
weight: 5
axis: Synthesis of Information

criterion: The response discusses at least 3 disadvantages, problems and pain points of using and developing MCPs and how to overcome them (e.g., (1) Security risks such as stealing tokens and taking over personal account like Gmail, (2) prompt injection attacks hiding malicious messages that will spur the system to perform harmful activities, (3) Ballooning Context Problem of storing arbitrary context, (4) LLM limitations, i.e., MCP will be as useful as the LLM it is connected to).
weight: 4
axis: Synthesis of Information

criterion: The response claims higher efficiency than other tools or APIs without actual benchmarking (e.g., States using the GitHub MCP server are 20% faster in terms of latency than the API version without any proof).
weight: -4
axis: Miscellaneous

criterion: The response mentions the role of open source contributions or community involvement in the growth of MCP servers.
weight: 2
axis: Implicit Criteria

criterion: The response includes at least 5 sections covering all the important questions asked by the user (i.e., Why are MCP servers for AI agents a buzz now, difference of MCP from API, most commonly used MCP servers, most commonly used AI agents or applications using MCP servers, how to develop these servers, how prominent will they be in the near future, domains where this is useful, and if this can automate entire human workloads).
weight: 3
axis: Instruction Following