What is Mcp Spec Assessments

mcp-spec-assessments is an evaluation framework that assesses the quality and status of the Model Context Protocol (MCP) specification as a technical standards document, utilizing advanced Large Language Models (LLMs).

Use cases

Use cases for mcp-spec-assessments include evaluating new versions of the MCP specification, ensuring compliance with technical standards for AI applications, and providing feedback for improving the quality of the MCP documentation.

How to use

To use mcp-spec-assessments, users can input the MCP specification documents into the assessment framework, which will analyze the content and provide insights regarding its quality and adherence to technical standards.

Key features

Key features of mcp-spec-assessments include automated quality assessments, integration with state-of-the-art LLMs, and the ability to evaluate technical standards compliance of the MCP specification.

Where to use

mcp-spec-assessments can be used in various fields such as software development, AI research, and technical documentation to ensure that the MCP specification meets industry standards.

mcp-spec-assessments

Assessments of the quality of the Model Context Protocol (MCP) specification, regardings status as a technical standards document, conducted using state-of-the-art Large Language Models (LLMs).

Model Context Protocol (MCP)

Model-Context-Protocol (MCP), as described by MCP, on its website and GitHub organization:

The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need.

MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.

MCP (Model Context Protocol) is a standard way for AI applications and agents to connect to and work with your data sources (e.g. local files, databases, or content repositories) and tools (e.g. GitHub, Google Maps, or Puppeteer).

Think of MCP as a universal adapter for AI applications, similar to what USB-C is for physical devices. USB-C acts as a universal adapter to connect devices to various peripherals and accessories. Similarly, MCP provides a standardized way to connect AI applications to different data and tools.

Before USB-C, you needed different cables for different connections. Similarly, before MCP, developers had to build custom connections to each data source or tool they wanted their AI application to work with—a time-consuming process that often resulted in limited functionality. Now, with MCP, developers can easily add connections to their AI applications, making their applications much more powerful from day one.

Model Context Protocol (MCP) Assessments

This repository documents attempts to leverage state-of-the-art Large Language Models (LLMs) to assess the Model Context Protocol (MCP) specification itself, not for with respect to its overall utility, or functionality per se, but rather to evaluate the clarity, completeness, and correctness of the specification itself as a technical document. The ./assessments directory conatains the results of these assessments.

For the existing assessments, a project was created in Claude Desktop, and the following resources were added to the project knowledge:

• A downloaded copy of the schema.ts file from the MCP GitHub repository.
• A downloaded copy of the schema.json file from the MCP GitHub repository.
• A copy of the textual content of the website pages located under: https://modelcontextprotocol.io/specification/2025-03-26. The markdown content of these pages was collated into a single file using a simple script, and the file itself can be found here.

Project Instructions

The main goal is for Claude to analyze and assess the quality of the Model-Context-Protocol (MCP) specification.

NOTE: "MCP" will always denote "Model-Context-Protocol" a communication standard for connecting AI applications.

The entire spec (latest version aka 2025-03-26) is comprised of these resources and these only:
- The documents located on the website https://modelcontextprotocol.io/specification/2025-03-26 and the sub-sections therein.
- The schema that describes the messages and data types, found in the TypeScript `schema.ts` (https://github.com/modelcontextprotocol/modelcontextprotocol/blob/main/schema/2025-03-26/schema.ts)
- The JSON Schema, which is auto-generated from the TypeScript schema, and hence can be assumed to be identical in content, but different in form.
- CAVEAT: The protocol states that its message conform to JSON-RPC 2.0. Therefore, the JSON-RPC 2.0 spec can also be considered a corollary, but authoritative resource.

These documents have also been uploaded to the Project Knowledge. Note that the "mcp-spec-docs.txt" file is a complete collation of the specification pages found on the website, so you may refer to that instead of querying the website or GitHub if you prefer.

When evaluating the standard, you may consult any other resources that you think might be helpful, to gain additional context, but the standard itself must be considered SELF-CONTAINED. That is, if there is an issue discovered in the spec, which appears to be clarified in a GitHub Issue discussion, e.g., then unless that has actually been incorporated or reflected in the spec documents themselves, you must consider this an active problem. When reviewing the spec, consider the following questions:
- Is this term thoroughly, comprehensively, and formally defined?
- Would someone implementing a client, a server, or an SDK know exactly what is required by the spec regarding the overall implementation, or regarding any specific functionality?
- Does the spec deviate from JSON-RPC 2.0 standards at all?
- Is the specification clear enough such that an automated analysis tool be developed to evaluate compliance?

Prompt

Each assessment was initiated with the following prompt:

I have stated the entire goal of this project in the Project Knowledge and instructions - I would like you provide a comprehensive review of the quality of the Model-Context-Protocol specification. Please review the instructions and let me know if you have any questions before proceeding.

One assessment was conducted using Claude Opus 4, while the other was conducted using Claude Sonnet 4 with Extended Thinking and Research Mode enabled. I intend to conduct further asssessments, exploring other models, and perhaps other instructions as well.