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Server Quality Checklist

58%
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  • Latest release: v0.0.1

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'who-health' has a clearly defined purpose focused on WHO Global Health Observatory operations, making it impossible for an agent to misselect between non-existent alternatives.

    Naming Consistency5/5

    A single tool inherently exhibits perfect naming consistency as there are no other tools to compare against. The name 'who-health' follows a clear and descriptive pattern that aligns with the server's purpose, with no deviations or mixed conventions present.

    Tool Count2/5

    A single tool is generally too few for a server's purpose unless it is extremely narrow, but here the tool description suggests broad capabilities (access health indicators, country statistics, regional data, etc.). This likely represents a significant under-scoping, as typical data access servers benefit from multiple specialized tools for different query types or operations.

    Completeness3/5

    The tool claims to provide comprehensive access via OData queries, which could theoretically cover many operations, but having only one tool may create gaps in usability or functionality. For example, there are no dedicated tools for common actions like listing available datasets, filtering by specific criteria, or managing queries, which might hinder agent workflows despite the broad OData coverage.

  • Average 2.9/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 0 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
  • 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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'Provides access to comprehensive health data' and mentions OData API usage, but fails to disclose critical traits: whether it's read-only or mutative, authentication requirements, rate limits, error handling, or response formats. For a tool with 12 parameters and no output schema, this is a significant gap in transparency.

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

    Conciseness4/5

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

    The description is appropriately sized and front-loaded, starting with the unified purpose and key features. Both sentences earn their place by explaining the tool's scope and technical approach. However, it could be slightly more concise by integrating the OData mentions more seamlessly, but overall it's efficient with minimal waste.

    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?

    Given the tool's complexity (12 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits, usage guidelines, and output expectations, which are crucial for an agent to invoke it correctly. While it covers the purpose and data scope, it doesn't compensate for the missing structured information, leaving significant gaps in understanding.

    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 schema already documents all parameters thoroughly. The description adds marginal value by mentioning 'OData query syntax' and examples of data topics, but doesn't provide additional parameter semantics beyond what's in the schema. This meets the baseline for high schema coverage without compensating with extra insights.

    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?

    The description clearly states the tool's purpose: 'access health indicators, country statistics, and regional data via the modern OData API' and specifies it's for WHO Global Health Observatory operations. It distinguishes the scope ('comprehensive health data from the World Health Organization') and examples of topics covered. However, with no sibling tools mentioned, it doesn't need to differentiate from alternatives, so it's not a 5.

    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?

    The description provides no guidance on when to use this tool versus alternatives or any prerequisites. It mentions 'via the modern OData API' and 'using standard OData query syntax,' which gives some technical context, but lacks explicit usage scenarios, exclusions, or comparisons to other methods. This leaves the agent with minimal direction on appropriate application.

    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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  • Confirm that the MCP server is working as expected.
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  • Evaluate tool definition quality.

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