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Server Quality Checklist

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  • Latest release: v1.0.1

  • Disambiguation5/5

    The two tools have completely distinct purposes: gemini_info handles diagnostics and system information, while gemini_query handles querying the Gemini model. There is no overlap in functionality that could cause confusion.

    Naming Consistency5/5

    Both tools follow a consistent gemini_ prefix pattern with clear descriptive suffixes (info and query). The naming is uniform and predictable across the toolset.

    Tool Count2/5

    With only two tools, the server feels under-scoped for a CLI interface to Gemini. A typical CLI would include more operations like session management, configuration handling, or batch processing, making this set feel incomplete for the domain.

    Completeness2/5

    The toolset is severely incomplete for a Gemini CLI interface. It lacks essential operations such as managing sessions, configuring settings, handling file uploads beyond references, or listing available models. The two tools cover only basic diagnostics and querying, leaving significant gaps in typical CLI workflows.

  • Average 3.7/5 across 2 of 2 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 is failing
  • 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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  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
      "$schema": "https://glama.ai/mcp/schemas/server.json",
      "maintainers": [
        "your-github-username"
      ]
    }

    Then . Browse examples.

  • Add related servers to improve discoverability.

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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

  • Behavior3/5

    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 implies read-only behavior through terms like 'diagnostics' and 'list', but fails to disclose output formats, error conditions, or whether these operations are cached/live. It meets minimum expectations for a metadata tool but lacks rich behavioral context.

    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 sentence that front-loads the category ('Gemini CLI diagnostics') and follows with a colon-delimited list of specific capabilities. No words are wasted.

    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?

    For a simple single-parameter tool with complete schema coverage, the description is adequate. It covers the primary use cases (though misses list_models in prose). No output schema exists, but the description sufficiently indicates the return type nature (diagnostic info).

    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?

    With 100% schema coverage, the baseline is 3. The description maps most enum values to user-friendly intents (ping→connectivity, etc.) but omits 'list_models' entirely. It adds minimal semantic value beyond what the schema's detailed descriptions already provide.

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

    Purpose4/5

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

    The description clearly identifies the tool's purpose using specific verbs (check, get, list) and identifies the resource (Gemini CLI diagnostics). It implicitly distinguishes from sibling 'gemini_query' by focusing on introspection/metadata rather than active querying, though explicit differentiation would strengthen this further.

    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 lists capabilities but provides no explicit guidance on when to use this tool versus 'gemini_query'. It lacks prerequisites (e.g., 'use ping to verify connectivity before querying') or exclusion criteria that would help an agent select the correct tool.

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

  • Behavior3/5

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

    With no annotations, description carries full burden. It discloses authentication method (locally authenticated CLI), safety controls (sandbox, yolo, approval modes), and file referencing (@path). However, misses return format, error behavior, and side effects of auto-approve modes.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Front-loaded with core purpose, followed by model support, file syntax, and options list. Efficiently packs 8 parameters worth of context into four brief statements. Minor deduction for the slightly telegraphic 'Options:' list which could flow better.

    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?

    Covers key capabilities well for a complex 8-parameter tool: authentication, model flexibility, file inclusion, workspace directories, and safety modes. Absence of output schema is mitigated by clear description of what the tool does, though mention of return format would improve completeness.

    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?

    Adds conceptual value beyond 100% schema coverage by explaining @path syntax for file references and grouping related boolean flags (sandbox, yolo) under 'Options'. Schema handles individual parameter docs; description provides usage context.

    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?

    States specific action (Send), resource (prompt to Google Gemini), and mechanism (locally authenticated CLI). Clearly distinguishes from sibling 'gemini_info' by focusing on active querying vs. information retrieval.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Implies usage through 'Send a prompt' and lists capabilities, but lacks explicit guidance on when to use gemini_info instead, or when-not-to-use scenarios (e.g., when file contexts are inappropriate).

    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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  • Evaluate tool definition quality.

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