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enesbol

GCP MCP Server

by enesbol
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Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap between tools. The single tool 'test_gcp_auth' has a clear and distinct purpose that cannot be mistaken for any other tool in this set.

    Naming Consistency5/5

    The naming pattern is perfectly consistent as there is only one tool. It follows a verb_noun structure ('test_gcp_auth'), and with no other tools to compare, there are no deviations or inconsistencies in naming conventions.

    Tool Count2/5

    The tool count of 1 is too few for a server named 'GCP MCP Server', which suggests a broad scope covering Google Cloud Platform services. A single authentication test tool is insufficient for meaningful interaction with GCP resources, making this a significant mismatch in scope.

    Completeness1/5

    The tool surface is severely incomplete for a GCP server. It only provides an authentication test, lacking any operations for core GCP services like compute, storage, databases, or management. This leaves major gaps that will prevent agents from performing useful tasks in the domain.

  • Average 2.9/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • 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.

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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

  • Behavior2/5

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

    No annotations are provided, so the description carries full burden. It mentions 'test' but doesn't disclose behavioral traits like what gets tested (e.g., credentials, permissions), whether it's safe/destructive, or what output to expect. This leaves significant gaps for a tool with zero annotation coverage.

    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 extremely concise ('Test GCP authentication') with no wasted words, making it front-loaded and easy to parse. It efficiently conveys the core purpose in minimal text.

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

    Completeness2/5

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

    Given the tool's complexity (simple auth test) but lack of annotations and output schema, the description is incomplete. It doesn't explain what 'test' entails, what success/failure looks like, or any behavioral context, leaving the agent with insufficient information.

    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 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description doesn't add param info, but that's acceptable here, meeting the baseline for zero-param tools.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'Test GCP authentication' states a clear purpose (testing authentication) but lacks specificity about what resource or system is being tested. It doesn't distinguish from siblings (though none exist), making it somewhat vague but functional.

    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 provides no guidance on when to use this tool, such as during setup, troubleshooting, or validation scenarios. With no siblings, differentiation isn't needed, but general usage context is missing.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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