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cindyhsugit

mcp-server-simpsons-cities

by cindyhsugit
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

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 one tool, there is no possibility of confusion or overlap. The tool's purpose is clearly distinct.

    Naming Consistency5/5

    The single tool name 'get_simpsons_city' follows a clear verb_noun pattern, which is consistent throughout the server.

    Tool Count3/5

    The server has only 1 tool, which is borderline for scope. While the narrow purpose could justify a single tool, the count is minimal and might feel thin if users expect additional related operations.

    Completeness5/5

    The server's domain is specifically finding Simpsons-related cities near a zip code. The single tool directly fulfills this purpose with no obvious missing functionality or dead ends.

  • Average 4.5/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 3 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

  • Behavior4/5

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

    No annotations provided, so the description must carry the behavioral burden. It states the tool 'finds' (implying a read-only operation), and adds the free-tier max radius constraint. It does not discuss error handling or rate limits, but for a simple search the disclosure is adequate.

    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 two sections: a one-sentence purpose and a compact Args list. Every sentence is informative, and the formatting is clear and scannable. No filler or redundancy.

    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?

    The output schema covers return values, so the description only needs to cover inputs and usage. It does this well by explaining the search criteria, default radius, and free-tier limit. Minor gaps like invalid zipcode behavior are acceptable for this type of tool.

    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?

    The input schema has zero descriptions, but the description compensates by specifying that zipcode is a 5-digit US zip code, radius_km is in kilometers with a default of 30 and a free-tier max of ~30. This adds critical meaning beyond the raw schema types.

    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 specifies the verb (Find), the resource (real cities near a US zip code), and the unique filter (share a name with The Simpsons elements). This fully distinguishes the tool from any potential alternatives, even though none are listed.

    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?

    Provides clear context: searches near a given zip code, radius_km default and free-tier max. No exclusions or alternatives are mentioned because there are no sibling tools, but the usage context is sufficient for an agent to know when to invoke 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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  • 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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