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mambalabsdev

mcp-icp-fit-scorer

by mambalabsdev
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.5

  • Disambiguation5/5

    With only one tool, there is no risk of confusion between tools. The tool's purpose is clearly defined in its description.

    Naming Consistency5/5

    A single tool name cannot be inconsistent. The name 'score_icp_fit' follows a clear verb_noun pattern.

    Tool Count2/5

    A single tool feels insufficient for a server dedicated to ICP fit scoring. The domain likely requires additional tools for managing ICP templates, historical scores, or configuration.

    Completeness2/5

    The tool covers only the scoring operation. Missing are tools for creating or managing ICP templates, listing past scores, or configuring signals.

  • Average 4.4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 26 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 passing

  • 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?

    The description reveals key behavioral traits beyond annotations: read-only nature, requirement for APIFY_TOKEN, credit consumption, and output structure. It also clarifies the optional LLM usage for icp_description. This adds value on top of the readOnlyHint annotation. However, it does not discuss rate limits or error behavior.

    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 concise, with two sentences. The first sentence states purpose and output; the second covers input methods and requirements. No wasted words, and key information is 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 the complexity (9 parameters, no output schema), the description provides a good overview of inputs and outputs. It mentions the return format (icp_score, icp_tier, per-signal breakdown) and optional explanation. However, it omits details on the exact structure of the per-signal breakdown or scoring_config format, which are left to the schema. Overall, it is fairly 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?

    Schema coverage is 100%, so baseline is 3. The description adds meaning by explaining the relationship between template, scoring_config, icp_description, and llm_api_key. It also clarifies the purpose of fetch_signals and include_explanation. This additional context improves parameter understanding.

    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's purpose: scoring a company against an ICP using weighted signals. It specifies the output format (icp_score, icp_tier, per-signal breakdown) and input options. With no sibling tools, the purpose is unambiguous and distinct.

    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?

    The description provides clear guidance on how to define the ICP (template, JSON, or plain-English description) and notes prerequisites (APIFY_TOKEN, credits). It also explains optional parameters like fetch_signals and include_explanation. While it doesn't explicitly state when not to use alternatives, the guidance is sufficient given no sibling tools.

    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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