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MCP-Mirror

GCP MCP Server

by MCP-Mirror
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Server Quality Checklist

50%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'test_gcp_auth' has a clearly distinct and singular purpose.

    Naming Consistency5/5

    The naming follows a consistent snake_case pattern with a verb_noun structure ('test_gcp_auth'). With only one tool, consistency is inherently perfect as there are no other tools to compare against.

    Tool Count2/5

    A single tool is too few for a server named 'GCP MCP Server', which implies broader Google Cloud Platform functionality. This minimal toolset severely limits the server's utility and scope, making it feel incomplete and underpowered for its apparent domain.

    Completeness1/5

    The tool surface is severely incomplete for a GCP server. It only provides authentication testing, with no tools for core GCP operations like managing compute instances, storage, databases, or other cloud services, leaving significant gaps that will cause agent failures.

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

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

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  • This repository includes a README.md file.

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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 the full burden. It only states the action ('test') without disclosing behavioral traits like what gets tested (e.g., credentials, permissions), the output format, error conditions, or side effects. This is inadequate 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 a single, efficient phrase with no wasted words. It's front-loaded and appropriately sized for a simple tool, making it easy to parse quickly.

    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 no annotations, no output schema, and a simple purpose, the description is incomplete. It doesn't explain what 'test' means in practice, what results to expect, or any prerequisites, leaving significant gaps for an AI agent to understand the tool's behavior.

    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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add param info, which is appropriate, earning a baseline score of 4 for not introducing confusion or redundancy.

    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 action (test) and target (GCP authentication), but it's vague about what 'test' entails—does it validate credentials, check permissions, or verify connectivity? With no siblings, differentiation isn't needed, but the purpose could be more specific.

    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?

    No guidance is provided on when to use this tool—for example, after configuration changes, before other operations, or for troubleshooting. With no sibling tools, alternatives aren't relevant, but the description lacks any context for its application.

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

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

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