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bdc-assist

bdc-doc-mcp

by bdc-assist
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only a single tool, there is no possibility of ambiguity. The tool has a clearly defined purpose for searching documentation.

    Naming Consistency5/5

    The tool name 'search_docs' follows a consistent verb_noun pattern and is descriptive. Since it is the only tool, naming is inherently consistent.

    Tool Count1/5

    The server exposes only one tool, which is extremely thin. Even though the tool is multi-functional, a single tool does not constitute a well-scoped set; most servers with this purpose would benefit from at least a couple of complementary tools (e.g., retrieving a document by ID or listing available types).

    Completeness3/5

    The search tool covers multiple documentation sources and provides filtering and multiple modes, which addresses the core purpose. However, it lacks any other operation such as fetching a specific document, listing available doc types, or managing content, leaving notable gaps for a documentation server.

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

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

    • No community issues in the last 6 months
    • 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.

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

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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?

    With no annotations, the description fully discloses behavioral traits: default doc types when omitted, score interpretation (distance vs occurrences), the effect of date filters, and ranking by relevance not date. It also notes that only event and update docs carry dates, further clarifying behavior. No annotation contradiction exists.

    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 well-structured with bullet points for modes and types, and clear paragraphs for date and ranking behavior. It is lengthy but every sentence carries essential information, and it is front-loaded with the purpose and return content. 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?

    Given the tool's complexity (6 parameters, no output schema, no annotations), the description is complete. It explains return format, scoring meaning, type-specific behavior, and parameter interactions. It fully equips an agent to invoke the tool correctly for a variety of use cases.

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

    Parameters5/5

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

    Schema description coverage is 0%, so the description must compensate, and it does thoroughly. It explains query phrasing for each mode, k's range and purpose, mode options with detailed semantics, doc_type as a CSV list with each type's meaning, and date_from/date_to format and inclusive behavior. This adds far more meaning than the bare 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 the BDC (NHLBI BioData Catalyst) documentation database and returns top-k matching chunks with content, metadata, and a score. It names the specific resource and what is returned, making the tool's purpose unambiguous even without sibling tools for differentiation.

    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?

    Provides explicit guidance on when to use embedding vs keyword mode, how to adjust k for broad questions, when to explicitly name doc_type values, and how to use date filters for recency queries. It also warns that date filters implicitly narrow to types with dates and explains ranking behavior, giving clear when-to-use and when-not-to-use instruction.

    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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