Skip to main content
Glama
Leivoo

mcp-openapi-swagger

by Leivoo
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one fetches endpoint summaries from an OpenAPI spec, while the other sets the source URL for that spec. There is no overlap in functionality, making them easy to tell apart.

    Naming Consistency5/5

    Both tools follow a consistent naming pattern with the 'openapi_' prefix and descriptive action_noun structure (listEndpoints, setSource). The naming is uniform and predictable throughout the set.

    Tool Count2/5

    With only 2 tools, the server feels thin for its apparent purpose of interacting with OpenAPI/Swagger specs. Core operations like testing endpoints, validating specs, or detailed querying are missing, making the toolset underpowered for typical API exploration workflows.

    Completeness2/5

    The toolset is severely incomplete for OpenAPI/Swagger interaction. While it covers fetching endpoints and setting a source, it lacks essential operations such as testing API calls, retrieving detailed schema information, or validating specs, which are critical for comprehensive API exploration and integration.

  • Average 3.1/5 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 status not available

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

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

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 this sets a source URL for later use, implying a configuration or initialization action, but lacks details on permissions, persistence, error handling, or side effects. This is a significant gap for a tool that likely mutates state.

    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 that directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, making it highly concise and well-structured.

    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 lack of annotations and output schema, and the tool's likely role in configuration or state mutation, the description is insufficient. It doesn't explain what happens after setting the source, how errors are handled, or the scope of 'later calls', leaving critical gaps for agent 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?

    The schema description coverage is 100%, with the parameter 'sourceUrl' fully documented in the schema as a URI for OpenAPI JSON or Swagger UI. The description adds no additional semantic context beyond what the schema provides, so the baseline score of 3 is appropriate.

    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 action ('Set') and the resource ('OpenAPI/Swagger source URL'), specifying it's for configuring a source for subsequent operations. It doesn't explicitly differentiate from the sibling tool 'openapi_listEndpoints', which appears to be a read operation, but the purpose is well-defined.

    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 mentions 'for later calls', implying this tool should be used to configure a source before making API calls, but it provides no explicit guidance on when to use it versus alternatives, prerequisites, or exclusions. No comparison to the sibling tool is made.

    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?

    No annotations are provided, so the description carries the full burden. It discloses that the tool fetches and returns a summary, but lacks details on behavioral traits such as error handling (e.g., what happens with invalid URLs), caching behavior (e.g., how the cache is managed), rate limits, or authentication needs. The description is minimal and does not compensate for the absence of annotations.

    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 that front-loads the key action ('Fetch') and output, with no wasted words. It directly communicates the tool's function and parameter implication, making it easy to parse and understand quickly.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's moderate complexity (fetching and summarizing API specs), no annotations, no output schema, and 100% schema coverage, the description is minimally adequate. It covers the basic purpose and parameter use but lacks details on output format, error handling, or integration with the sibling tool, leaving gaps in completeness for effective agent use.

    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 fully documents the single parameter 'sourceUrl' as an optional URI. The description adds marginal value by clarifying that it's for 'OpenAPI JSON URL or Swagger UI URL' and mentions 'if omitted uses cached source', which provides context beyond the schema's format and optionality. This aligns with the baseline score of 3 when schema coverage is high.

    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 with a specific verb ('Fetch') and resource ('OpenAPI/Swagger spec'), and specifies the output format ('endpoints summary (method/path/summary/tags)'). It distinguishes from the sibling tool 'openapi_setSource' by focusing on fetching and summarizing rather than setting the source. However, it doesn't explicitly differentiate the scope or limitations compared to the sibling beyond the different action.

    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?

    The description implies usage by stating it fetches from a URL or uses a cached source, which suggests when to provide the 'sourceUrl' parameter. However, it lacks explicit guidance on when to use this tool versus the sibling 'openapi_setSource' (e.g., for initial setup vs. retrieval), and does not mention any prerequisites, exclusions, or alternative scenarios beyond the optional parameter.

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

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

APIMCP MCP server

Copy to your README.md:

Score Badge

APIMCP MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Leivoo/APIMCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server