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davidlandais

ovh-api-mcp

by davidlandais
OverviewSchemaRelated ServersScoreDiscussions
Rust
Hybrid

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.3.0

  • Disambiguation5/5

    execute and search have entirely different purposes: execute runs API calls, search explores the API spec. No overlap.

    Naming Consistency5/5

    Both tools are simple imperative verbs, consistent in style and lowercase. No mixing of conventions.

    Tool Count4/5

    Only two tools, but the server's design (search + execute) is focused and efficient for its purpose. Slightly minimal but still reasonable.

    Completeness5/5

    The combination of search (explore spec) and execute (make any API call) covers the full workflow of using OVH APIs. No obvious gaps.

  • Average 4.3/5 across 2 of 2 tools scored. Lowest: 3.7/5.

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

    • 0 of 1 community issues answered or closed in the last 6 months
    • 0 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 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.

  • This repository includes a glama.json configuration file.

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

    Then . Browse examples.

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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

  • Behavior3/5

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

    With no annotations, the description must disclose behavioral traits. It explains that the tool executes user-provided JavaScript code against the spec, but it does not mention safety implications, execution limits, or whether modifications are possible. Examples are all read-only, but this is not explicitly stated.

    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 front-loaded with the purpose and then provides necessary details including types and examples. While it is lengthy, the information is essential for correct usage, so it is not overly verbose.

    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?

    The description explains how to use the parameter and what the code should do, but it does not describe what the tool returns (e.g., the output format or handling of errors). Given the absence of an output schema, this leaves some ambiguity.

    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 schema description coverage is 100%, but the description adds extensive detail: the exact function signature, type definitions, and multiple code examples. This far exceeds the schema's simple description of 'JavaScript function to execute.'

    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 states 'Search the OVH API OpenAPI 3.1 spec' with a specific verb and resource. It distinguishes from the sibling tool 'execute' by focusing on spec search.

    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 does not explicitly state when to use this tool versus the sibling 'execute' or provide any alternatives. It only mentions that all configured services are included, which is implicit context.

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

  • Behavior5/5

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

    With no annotations, the description fully discloses behavior: authentication is automatic, errors throw for HTTP >= 400, code must be an async arrow function, and the exact API signature is provided. No contradictions.

    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 relatively long but well-structured with clear sections, code blocks, and examples. Each part serves a purpose; however, some repetition could be trimmed. It is appropriately sized for the complexity.

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

    Completeness5/5

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

    Given the single required parameter, no output schema, and the complexity of executing arbitrary JavaScript against an API, the description covers all necessary aspects: code format, available objects, authentication, error handling, and usage pattern. It is complete.

    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?

    The only parameter 'code' has a schema description but the tool description adds significant value: required format (async arrow function), the available `ovh` object with typed request method, and examples. Since schema coverage is 100%, baseline is 3, but the description elevates it to 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?

    The description clearly states 'Execute JavaScript against the OVH API' and distinguishes from the sibling tool 'search' by instructing to use 'search' first to find endpoints. This provides a specific verb-resource pairing and clear differentiation.

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

    Usage Guidelines5/5

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

    Explicitly states when to use (after searching) and provides a detailed code template, available API methods, error handling, and examples. It mentions the alternative tool 'search' and gives practical usage guidance.

    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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