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jason-chang

FastAPI OpenAPI MCP Server

by jason-chang
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one generates API call examples from an OpenAPI spec, while the other searches API endpoints with various filters. There is no overlap in functionality, making it easy for an agent to select the correct tool.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (generate_examples and search_endpoints), using snake_case throughout. The verbs 'generate' and 'search' clearly indicate their actions, maintaining a predictable and readable convention.

    Tool Count2/5

    With only 2 tools, the server feels thin for a FastAPI OpenAPI MCP server, which typically involves more operations like validating specs, listing endpoints, or generating documentation. This limited set may not cover the full scope expected for such a domain.

    Completeness2/5

    The tool surface has significant gaps for an OpenAPI-focused server. Missing are core operations such as retrieving or parsing the OpenAPI spec, validating endpoints, or generating documentation. While the existing tools are useful, they do not provide a complete workflow for interacting with OpenAPI specifications.

  • Average 2.9/5 across 2 of 2 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 is failing

  • 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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    Then . Browse examples.

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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. The description only states what the tool does at a high level ('生成各种格式的 API 调用示例'), but doesn't disclose any behavioral traits such as whether it's a read-only operation, if it has side effects, rate limits, authentication requirements, or what the output looks like. For a tool with 6 parameters and no annotations, 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.

    Conciseness5/5

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

    The description is a single, efficient sentence in Chinese that directly states the tool's purpose without any fluff. It's appropriately sized and front-loaded, with every word contributing to understanding the core functionality.

    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 complexity (6 parameters, no output schema, no annotations), the description is incomplete. It doesn't address behavioral aspects, output format, or usage context. While the schema covers parameters well, the description fails to provide the broader context needed for an agent to use this tool effectively, especially for a generation tool that likely produces structured output.

    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 input schema already documents all parameters thoroughly. The description adds no additional meaning about parameters beyond what's in the schema (e.g., it doesn't explain how 'path' and 'method' interact or what 'example_strategy' entails in practice). Baseline 3 is appropriate when the schema does the heavy lifting.

    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: '根据 OpenAPI 规范生成各种格式的 API 调用示例' (Generate API call examples in various formats based on OpenAPI specification). It specifies the verb '生成' (generate) and resource 'API 调用示例' (API call examples). However, it doesn't explicitly differentiate from its sibling tool 'search_endpoints', which might have overlapping functionality related to API endpoints.

    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. There's no mention of the sibling tool 'search_endpoints', nor any context about when this generation tool is appropriate versus searching or other actions. The agent must infer usage solely from the purpose statement.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

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

    With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions 'advanced search' and lists search methods, it doesn't describe what the tool returns (format, structure), whether it's paginated, rate limits, authentication requirements, or error conditions. For a search tool with 7 parameters and no output schema, this leaves significant behavioral gaps.

    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 a single, efficient sentence that communicates the core functionality without unnecessary words. It's appropriately sized for the tool's complexity and gets straight to the point about what the tool does. No sentences waste space or repeat information already available elsewhere.

    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?

    For a search tool with 7 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what gets returned (endpoint objects? just IDs?), how results are structured, whether there's pagination, or what happens with no matches. The agent lacks critical context needed to effectively use this tool despite the comprehensive input schema.

    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 fully documents all 7 parameters with descriptions, enums, defaults, and constraints. The description adds no parameter-specific information beyond what's in the schema. The baseline score of 3 reflects adequate but minimal value addition from the description regarding parameters.

    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 as an 'advanced search API interface' that supports multiple search methods (keywords, regex, tags, methods). It specifies the resource being searched (API endpoints) and the advanced capabilities. However, it doesn't explicitly differentiate from the sibling 'generate_examples' tool, which appears to serve a different function.

    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 in what context it should be applied. There's no mention of prerequisites, typical use cases, or comparison with the sibling 'generate_examples' tool. The agent receives no usage direction beyond the basic functionality description.

    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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