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vivz-git

GTM MCP Server

by vivz-git
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Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion between tools. The server_info tool has a clear and singular purpose, so disambiguation is trivially perfect.

    Naming Consistency5/5

    The single tool is named server_info, which follows a clear noun_noun pattern. With only one tool there are no inconsistencies to evaluate, so naming is internally consistent.

    Tool Count2/5

    A server titled 'GTM MCP Server' would be expected to expose a range of GTM operations, but it exposes only a single information reporting tool. This falls at the 'too few' end of the scale for the apparent scope.

    Completeness1/5

    The server provides no tools for actual GTM management—no operations for tags, triggers, variables, containers, or any data modification. It only reports capabilities, leaving the domain severely uncovered.

  • Average 4.9/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 2 commits in the last 12 weeks
    • No stable releases found
    • 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.

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

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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 declare readOnlyHint, idempotentHint, and destructiveHint, but the description adds valuable context: it takes no arguments, reads no customer data, changes nothing, and returns specific status categories. 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 concise, front-loaded with the core purpose, and every sentence adds value. It avoids redundancy, clearly separates what the tool returns from when to use it, and is appropriately sized for the tool's simplicity.

    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?

    With an output schema present, no parameters, and annotations covering safety traits, the description covers all necessary context: when to call it, what it returns, and what not to do. Nothing important is missing for an agent to invoke it correctly.

    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 tool has zero parameters, and the description explicitly states it takes no arguments, which is sufficient. With no parameters to document, the description does not need to add parameter semantics beyond confirming the absence of inputs.

    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 states a specific verb and resource: 'Report which GTM capabilities this server can currently perform.' It clearly identifies the tool's function and is not a tautology of its name or title. Even without siblings, it is unambiguous about what the tool does.

    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?

    The description explicitly tells the agent when to call this tool: first when unsure about capability availability, or after a data-modifying call fails to check whether writes are disabled. It also advises against calling tools listed under planned capabilities, providing actionable routing 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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