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higebu

cml2-mcp

by higebu
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.1

  • Disambiguation5/5

    The two tools have clearly distinct purposes: cml_api makes arbitrary REST calls to the CML2 API, while cml_openapi fetches the OpenAPI specification. There is no overlap in functionality.

    Naming Consistency5/5

    Both tools use the consistent prefix 'cml_' followed by a descriptive snake_case name ('api' and 'openapi'), following a clear pattern.

    Tool Count2/5

    With only two tools, the server is extremely minimal for what appears to be a full API wrapper. Most use cases would require the user to manually parse the OpenAPI spec and craft REST calls, making the tool set feel incomplete.

    Completeness2/5

    The server provides only a low-level API call tool and a spec fetcher, lacking any higher-level operations (e.g., CRUD for labs, devices, etc.). Users must implement all logic themselves, which is a significant gap for a CML2 integration.

  • Average 4.1/5 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
    • 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.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
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  ]
}

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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?

    With no annotations, description carries full burden. It discloses key behaviors: re-authenticates and retries on 401, raises on non-2xx, returns raw body text, expects JSON body. However, it does not cover rate limits or authentication details beyond re-auth.

    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?

    Description is reasonably concise with a clear first line, then Args section. Every sentence adds value, but slight redundancy ('Returns the raw response body as text' is repeated). Could be slightly more front-loaded.

    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?

    Given output schema exists, description doesn't need to detail return values. It covers method, path, body, auth retry, and error handling. For a generic API call, this is sufficiently complete, though pagination or response format details are missing.

    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?

    Schema description coverage is 0%, so description compensates fully. It explains method (HTTP verbs), path (after /api/v0, must start with '/'), and body (optional JSON, Content-Type set). Adds significant meaning beyond the bare schema.

    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 it is for authenticated CML2 REST calls, specifying method, path, and body. It implies direct API access but does not explicitly differentiate from sibling cml_openapi, lacking explicit sibling distinction.

    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?

    Provides clear context for making authenticated REST calls to CML2 but offers no guidance on when to use this tool versus alternatives (like cml_openapi) or when not to use it. No exclusions or prerequisites mentioned.

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

  • Behavior4/5

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

    The description discloses caching behavior (location, duration of 24h) and the ability to force a refresh with refresh=True. Since no annotations are provided, this fills a gap in transparency. It does not mention rate limits or authorization needs, but for a read-only retrieval tool this is sufficient.

    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 concise sentences. The first sentence states the primary purpose immediately, and the second adds caching details and the parameter behavior. Every sentence earns its place with no wasted words.

    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?

    Given the output schema exists (though not provided), the description adequately covers the return value and caching. It does not mention error conditions or output format details, but the presence of an output schema reduces the need for such detail. The description is reasonably complete for a simple retrieval tool.

    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 'refresh' is a boolean with default false, and the description explains it: 'Set refresh=True to force a re-fetch.' Although schema description coverage is 0%, the tool description compensates by clearly explaining the parameter's effect.

    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 the tool returns 'CML2's live OpenAPI spec as a JSON string.' This is a specific verb (return) and resource (OpenAPI spec), and it differentiates from the sibling tool 'cml_api' which presumably handles general API calls.

    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 when the OpenAPI spec is needed, but does not explicitly state when to use this tool versus the sibling 'cml_api' or provide exclusions. Usage guidelines are implied but not elaborated.

    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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