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zackscriven

lemma_docs_mcp

by zackscriven
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct roles: one performs ranked search/discovery over the corpus, the other retrieves full text for specific chunks or documents. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tool names follow the same `lemma_docs_<verb>` pattern, with context indicating search/discovery and get indicating retrieval. The naming convention is consistent and predictable.

    Tool Count3/5

    Two tools is on the thin side, but for a narrow local-documentation retrieval server the search-and-get pair is a reasonable minimal setup. It feels slightly sparse rather than fully fleshed out.

    Completeness4/5

    The server covers the core documentation workflow: discover relevant chunks and then expand them to full text. Notable gaps like listing all documents or browsing the corpus are acknowledged by the tool descriptions, and the search tool can surface paths, so agents can work around them.

  • Average 4.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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  • 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

  • Behavior5/5

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

    Beyond the readOnly/idempotent annotations, the description discloses important non-obvious behaviors: it is a local documentation index only, never calls live Lemma or banking APIs, cannot move money or mutate state, and excludes specific page areas. It also explains truncation and error conditions, adding substantial behavioral context beyond annotations.

    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?

    Although the description is long, it is well-structured with clear sections (coverage, args, returns, examples, errors) and every sentence contributes actionable information. The core purpose and key constraints are front-loaded before the parameter details.

    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?

    The description covers the full call contract: parameter semantics, routing behavior, return structure, pagination, examples, error handling, and explicit linkage to the sibling tool. Nothing needed for correct invocation or interpretation is missing.

    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%, so the baseline is 3, but the description adds meaningful enrichment: per-namespace examples mapping queries to areas, intent usage examples, a follow-up paging pattern with offset, and clarification of response_format. These examples go beyond the schema's property descriptions.

    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 opens with a specific verb and resource: 'Search the local Lemma (getlemma.com) documentation corpus and return focused, ranked source excerpts.' It clearly differentiates from the sibling lemma_docs_get by noting that chunks are capped and that the full text is obtained via lemma_docs_get.

    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?

    The description explicitly says to use 'auto' unless the corpus area is already known, and it names the sibling tool for retrieving full text. The coverage note instructs the agent to say so instead of guessing when a query falls outside the 8-page index, which is concrete when-not-to-use guidance.

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

  • Behavior5/5

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

    Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, but the description adds substantial behavioral context beyond that: it 'NEVER calls live Lemma or banking APIs,' unknown ids/paths are reported in 'missing' rather than failing the call, and truncation is possible with a message to follow. This is exactly the kind of non-obvious behavior an agent needs to know.

    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?

    The description is front-loaded with purpose and usage, then organized into Args, Returns, Examples, and Errors sections. Every section earns its place, and the content is dense but non-redundant with the schema.

    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?

    The description covers validation behavior, missing-id behavior, truncation, live-API boundaries, return shape, and examples for both access modes. Given the output schema exists and the annotations cover safety, nothing an agent needs to invoke this correctly is missing.

    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?

    The input schema already documents all three parameters with clear descriptions, so the baseline is 3. The description adds meaningful extras: the critical 'at least one of ids or path is required' constraint that is absent from the schema, the difference in what each mode returns, and concrete examples. This goes beyond the schema without having to carry the full parameter-documentation burden.

    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 opens with a specific verb-resource pair: 'Fetch the full text of specific indexed Lemma documentation chunks by id, or every chunk of one document by its relative path.' It clearly distinguishes the tool from its sibling lemma_docs_context by framing this tool as the expansion step, not the discovery step.

    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?

    The description explicitly says to use this tool after lemma_docs_context to expand excerpts and warns 'do not use it to browse (use lemma_docs_context for discovery).' This is direct, unambiguous routing guidance that names the alternative tool and the condition that selects it.

    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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  • Evaluate tool definition quality.

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