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mambalabsdev

mcp-gtm-signals-aggregator

by mambalabsdev
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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 possibility of confusion between tools. The tool's purpose is singular and clearly defined.

    Naming Consistency5/5

    The single tool name follows a clear verb_noun pattern (aggregate_gtm_signals), which is consistent by default.

    Tool Count2/5

    The server has only one tool, which is too few for the apparent scope of aggregating GTM signals. While the tool is comprehensive, it would benefit from being split into separate operations (e.g., for hiring signals and tech stack detection) or providing additional control tools.

    Completeness3/5

    The tool covers the main aggregation function, but there are likely gaps: no ability to fetch raw signals individually, no configuration or filtering options, and no tools for related actions like listing available signal types or checking data sources.

  • Average 4.5/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 25 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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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

  • Behavior5/5

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

    Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds valuable transparency beyond that: it notes the tool 'requires an APIFY_TOKEN and consumes Apify credits per call', which are important behavioral traits. There is no contradiction with 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 three sentences long, each sentence adding essential information: core purpose, what it runs and returns, and read-only/auth/credit requirements. It is front-loaded with the most important information and contains no unnecessary words.

    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 tool has three parameters, no output schema, and comprehensive annotations, the description provides sufficient context. It explains the composite output, mentions the two signals combined, and clarifies auth and cost. The description covers all necessary aspects for an agent to use the tool correctly.

    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 coverage is 100%, so the schema already fully describes the three parameters. The description does not add additional meaning beyond stating the output includes a composite score and optional summary, which indirectly relates to the boolean parameters. No extra semantic detail is provided.

    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 aggregates GTM signals into a composite score, specifying the verb 'aggregate' and resource 'GTM signals'. It also distinguishes itself by noting it runs both hiring-signal and tech-stack detection in a single call, making its purpose unambiguous.

    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 context on when to use the tool (to aggregate GTM signals) and includes important caveats: it is read-only, requires an APIFY_TOKEN, and consumes credits. However, it does not explicitly mention when not to use it or alternatives, but there are no sibling tools, so the guidance is adequate.

    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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