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titov-d

mcp-agentdocs

by titov-d
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Server Quality Checklist

67%
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  • Latest release: v0.1.3

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: list_topics lists all available documents, search_docs searches for specific content, and get_doc retrieves full document. No overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun snake_case pattern: list_topics, search_docs, get_doc. Easy to predict and understand.

    Tool Count4/5

    With only 3 tools, the set is minimal but covers the essential operations for a documentation server: browse, search, and retrieve. It is slightly under the typical range but still reasonable for a focused server.

    Completeness5/5

    The tool surface covers the full lifecycle of accessing curated documentation: listing all documents, searching for specific content, and retrieving full documents. No obvious gaps for the stated purpose.

  • Average 4.4/5 across 3 of 3 tools scored.

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

    • 0 of 1 community issues answered or closed in the last 6 months
    • No commit activity data available
    • No stable releases found
    • 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.

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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

  • Behavior3/5

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

    No annotations provided, so description carries full burden. Describes output fields but does not disclose any behavioral traits like pagination, permissions, or rate limits. Adequate for a simple list tool but could be richer.

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

    Conciseness5/5

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

    Two concise sentences, no fluff. First sentence states action and output, second gives usage guidance.

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

    Completeness5/5

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

    Tool has no parameters, no output schema, and is simple. Description fully covers what it does and when to use it, relative to siblings. Complete for its complexity.

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

    Parameters4/5

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

    No parameters in schema (coverage 100%), so description does not need to add param info. Baseline of 4 for zero-param case.

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

    Purpose5/5

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

    Clear verb 'List' and resource 'curated document', with specific fields (topic, source URL, last-verified date). Differentiates from siblings get_doc and search_docs by being a list overview.

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

    Usage Guidelines4/5

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

    Explicit guidance: 'Call this first to see what is covered before searching.' Provides context for when to use, though lacks explicit when-not or alternatives.

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

  • Behavior3/5

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

    No annotations provided; description does not disclose behavioral traits like auth requirements, rate limits, or potential errors. It mentions 'verified' but lacks detail on what that entails.

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

    Conciseness5/5

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

    Two concise sentences, front-loaded with the core action followed by usage guidance. No extraneous words.

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

    Completeness4/5

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

    For a simple one-parameter tool with no output schema, the description covers purpose, source of input, and when to use. Could mention error handling or format assurance.

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

    Parameters4/5

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

    Schema describes 'id' with example; description adds context that the id comes from list_topics or search_docs, adding value beyond the schema.

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

    Purpose5/5

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

    The description clearly states the verb 'Fetch', the resource 'full curated markdown of one document', and specifies the id source from sibling tools (list_topics or search_docs), distinguishing it well.

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

    Usage Guidelines5/5

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

    Explicitly says when to use: 'when a snippet is not enough and you need the complete, verified document.' Also indicates the id comes from sibling tools.

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

  • Behavior4/5

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

    With no annotations, the description carries behavioral disclosure fully. It states the search returns 'ranked snippets with their source URLs' and emphasizes the data is 'curated, source-verified' and 'current, accurate', which is sufficient for a read-only search tool.

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

    Conciseness5/5

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

    Two concise sentences: the first defines purpose and scope, the second gives usage guidance and return format. No wasted words.

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

    Completeness5/5

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

    For a search tool with no output schema, the description adequately covers return type (ranked snippets with URLs) and context (curated docs, topics). It is complete for effective agent usage.

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

    Parameters4/5

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

    Schema coverage is 100% with good parameter descriptions. The description adds value by providing an example query ('streamable http transport auth') and explaining the optional topic filter with possible values, enhancing the schema.

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

    Purpose5/5

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

    The description clearly states it searches curated docs for MCP servers and Claude agents, specifying the scope (MCP, Claude Agent SDK, Claude Code) and distinguishing from sibling tools like get_doc (full doc retrieval) and list_topics (topic listing).

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

    Usage Guidelines4/5

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

    The description explicitly advises using this tool for current, accurate details instead of relying on memory, providing clear when-to-use guidance. However, it does not explicitly mention when not to use or directly name sibling alternatives.

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

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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.

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