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verdochello

verdoc-mcp

by verdochello
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Server Quality Checklist

67%
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  • Latest release: v0.1.1

  • Disambiguation4/5

    The two tools have distinct outputs: one generates an AGENTS.md file, the other returns structured facts and a score. There is some overlap in the underlying data (toolchain, directory layout), but the purpose and return type are clear enough to avoid serious confusion.

    Naming Consistency3/5

    Both tools share the 'verdoc_' prefix, but the suffixes are inconsistent: 'agents_md' is a noun-phrase while 'scan' is a verb. They are readable and predictable, but not a strict verb_noun pattern.

    Tool Count3/5

    With only two tools, the server feels thin, but it is scoped to a narrow domain (analyzing GitHub repos). The small count is not unreasonable, though it leaves room for more related tools.

    Completeness4/5

    The domain is repository analysis, and the two tools cover the core operations: generating an AGENTS.md guide and performing a detailed scan. Minor gaps exist (e.g., no direct metadata-only fetch), but for the stated purpose the surface is reasonably complete.

  • Average 4.2/5 across 2 of 2 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
    • 2 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • 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?

    With no annotations, the description discloses determinism, cost, and the public repo constraint. It doesn't discuss failure modes, auth, or rate limits, but for a read-only public scan the disclosed traits are reasonably reassuring. Lacks deeper behavioral nuance like output structure edge cases.

    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?

    A single, information-dense sentence that packs all key details—output fields, determinism, and cost—without redundant wording. Front-loaded with the primary purpose, making it easy to scan.

    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?

    Given no output schema, the description thoroughly enumerates expected return contents (toolchain commands, phantom_paths, etc.) and adds determinism/cost context. It doesn't describe exact data structures, but that is acceptable for a fact-collection tool. The single parameter and input schema are fully covered.

    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?

    Schema description coverage is 100% for the single 'repo' parameter, explicitly defining it as a GitHub 'owner/name' or URL. The description adds no new parameter details, so baseline 3 applies.

    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 states a clear action and resource: 'Structured facts about a public GitHub repository' with a detailed list of content types. It distinguishes itself by highlighting deterministic, non-LLM behavior and cost, setting it apart from a typical analysis tool.

    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?

    The description implies usage for deterministic repository analysis, noting 'Deterninistic; no language model' and cost. However, it does not explicitly mention when to use it over the sibling tool verdoc_agents_md, nor any exclusions or prerequisites beyond 'public GitHub repository'.

    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?

    With no annotations provided, the description carries the full burden. It discloses key behavioral traits: every path is checked against the git tree, ambiguous items are marked unverified rather than guessed, and the cost in USDC. This gives the agent a strong understanding of how the tool behaves.

    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 efficiently structured: it front-loads the main purpose, then adds verification behavior, usage guidance, and cost in separate short sentences. Every sentence adds value, and there is no unnecessary fluff.

    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?

    Given the tool has no output schema and no annotations, the description is remarkably complete. It covers what the tool produces, how it verifies data, when to use it, and the cost. The format parameter covers return details, so no gap is apparent.

    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?

    Schema description coverage is 100%, so the baseline is 3. The description does not add parameter-specific meaning beyond the schema; it mentions 'public' repo and cost but these are not parameter semantics. Therefore it stays at the baseline.

    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 tool's purpose: 'Generate an AGENTS.md for a public GitHub repository' with specifics about content (build/test commands, directory map, entry points, edit boundaries). This is specific and distinguishes it from the sibling tool verdoc_scan, especially with the usage note.

    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?

    Explicitly says 'Use this before working in a repository you do not already know,' providing clear context for when to use it. It does not name alternatives or exclusions, but the guidance is specific and actionable, so it earns a 4 rather than a 5.

    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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