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mihir19

bioevidence-mcp

by mihir19
OverviewSchemaRelated ServersScoreDiscussions
Python

Server Quality Checklist

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

  • Latest release: v0.1.1

  • Disambiguation5/5

    The health and search tools serve completely different purposes—one for service health, the other for querying evidence—so there is no ambiguity.

    Naming Consistency2/5

    One tool uses a short noun ('health'), while the other uses a verb_noun pattern with underscores ('search_biomedical_evidence'), showing inconsistent naming conventions.

    Tool Count2/5

    With only two tools and one being a simple health check, the server feels underdeveloped for the domain of biomedical evidence retrieval, suggesting more tools are needed.

    Completeness2/5

    The search tool provides a broad query capability, but lacks supporting tools for retrieving specific evidence items, managing sources, or performing other typical operations in the domain.

  • Average 3.3/5 across 2 of 2 tools scored. Lowest: 2.6/5.

    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
    • 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.

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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

  • Behavior2/5

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

    With no annotations, the description must disclose behavioral traits such as safety (read-only vs. destructive), authentication needs, or side effects. It only lists generic actions; for example, it doesn't clarify if 'score' and 'group' imply state changes or are purely analytical.

    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?

    Single sentence is concise and front-loaded with the core action, but it omits critical details that could be included without length increase. It earns its place but is insufficiently informative.

    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?

    Despite having an output schema (unseen), the description lacks context on data sources, scoring methodology, grouping logic, and summary format. For a tool with 3 parameters and no annotations, the description is too sparse to fully understand its capabilities.

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

    Parameters2/5

    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. 'optionally summarize' hints at include_summary, but 'query' and 'max_results_per_source' are not explained. Additionally, 'group' is mentioned but no grouping parameter exists, causing confusion.

    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?

    Description uses specific verbs (retrieve, score, group, summarize) and resource (biomedical evidence), giving a clear sense of functionality. However, it lacks differentiation from the sibling tool 'health' and doesn't specify what constitutes 'evidence' (e.g., literature types, databases).

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

    Usage Guidelines2/5

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

    No guidance on when to use this tool versus the sibling 'health' or other alternatives. The description does not state prerequisites or contextual cues for invocation.

    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 indicates 'stable' metadata, implying non-destructive and consistent behavior. However, additional details like rate limits or performance impact are absent.

    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?

    Description is a single, concise sentence with no wasted words. It effectively communicates the tool's function.

    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?

    Tool is simple with no parameters and an output schema present. The description is adequate for a health endpoint, though it could mention typical use cases like monitoring.

    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 has 0 parameters with 100% coverage. Description adds no parameter information, which is acceptable as there are none. Baseline is 4.

    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?

    Description clearly states the tool returns stable service health metadata. Distinguishes from sibling tool 'search_biomedical_evidence' which has a different purpose.

    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 explicit when-to-use or when-not-to-use instructions. Usage is implied as a health check, but no alternatives or exclusions are mentioned.

    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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