Skip to main content
Glama
MCPJam

MCP Specification Server

by MCPJam
OverviewSchemaRelated ServersScoreDiscussions
JavaScript

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.1.1

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion between tools, so disambiguation is perfect.

    Naming Consistency5/5

    The single tool name follows a clear pattern (prefix_verb_noun) and is internally consistent.

    Tool Count3/5

    One tool is borderline thin for a server; while it may be sufficient for a simple search utility, typical specification servers offer more operations.

    Completeness2/5

    The server only provides search, missing obvious operations like retrieving specific sections or listing contents, making the toolset incomplete for comprehensive specification interaction.

  • Average 2.8/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior1/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    There are no annotations, and the description provides no behavioral information such as what the tool returns (e.g., text, citations), side effects, or error scenarios. For a search tool, this is a significant omission; the agent knows it searches but not what the output looks like.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is one sentence of eight words, which is concise but perhaps too minimal. It could benefit from a bit more detail without becoming verbose. It is front-loaded but overly sparse.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the absence of an output schema and annotations, the description should compensate by explaining what the tool returns. It does not. The schema's enum helps, but the description does not clarify that the output is the content of the selected section. The tool feels incomplete for an agent to use confidently.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema has 100% description coverage with a clear description for the parameter. The tool description adds no additional semantic value beyond what the schema already provides, so the baseline score of 3 is appropriate.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states it searches the MCP specification document, but does not explicitly indicate that the search is restricted to the predefined sections listed in the enum. The combination of the description and the schema's enum makes the purpose clear, but the description alone could be slightly more precise.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No usage guidelines are provided. Since there are no sibling tools, the guidance is not strictly needed, but the description does not mention when to use this tool or any limitations. The agent can infer usage from the context, but the lack of explicit guidance is a slight gap.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

mcp-spec MCP server

Copy to your README.md:

Score Badge

mcp-spec MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/MCPJam/mcp-spec'

If you have feedback or need assistance with the MCP directory API, please join our Discord server