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trevoraspencer

geminicli-mcp

by trevoraspencer
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation4/5

    The two tools have clearly distinct purposes: one handles simple prompts, while the other adds a separate context block. The descriptions explicitly differentiate them, making misselection unlikely. However, the core functionality is similar, which could cause minor confusion in edge cases.

    Naming Consistency5/5

    Both tools follow a consistent 'gemini_prompt' prefix with descriptive suffixes ('with_context'), using snake_case throughout. The naming pattern is predictable and clearly indicates their relationship and differences.

    Tool Count3/5

    With only 2 tools, the server feels thin for a CLI interface, potentially lacking operations like configuration management, history, or batch processing. While the tools cover basic prompt execution, the scope seems limited compared to typical CLI toolkits.

    Completeness2/5

    The server is severely incomplete for a Gemini CLI domain, missing essential operations such as model selection, parameter tuning, file handling, conversation history, or error management. Agents will face dead ends when trying to perform common CLI tasks beyond basic prompting.

  • Average 3.6/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 5 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 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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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
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  ]
}

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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 discloses key behavioral traits: statelessness, context prepending inside a <context> tag, and execution in the MCP server's current working directory. However, it lacks details on permissions, rate limits, error handling, or output format, which are important for a CLI invocation tool with no output schema.

    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 highly concise and well-structured: three sentences that efficiently cover purpose, behavior, and execution context without redundancy. Each sentence adds distinct value, and it's front-loaded with the core functionality.

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

    Completeness3/5

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

    Given the tool's moderate complexity (3 parameters, no annotations, no output schema), the description is partially complete. It covers the basic operation and statelessness but lacks details on output format, error conditions, or model-specific behaviors. Without an output schema, the description should ideally hint at return values or response structure, which it doesn't, leaving gaps for an AI agent.

    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 description coverage is 100%, so the schema already documents all three parameters (prompt, context, model) with descriptions. The description adds minimal value beyond the schema, mentioning that context is 'prepended to the prompt inside a <context> tag' and that the tool is stateless, but doesn't provide additional syntax, format, or usage details for the parameters. This meets the baseline of 3 for high schema coverage.

    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 states the tool's purpose: 'Invoke the headless Gemini CLI with a prompt plus a separate context block.' It specifies the verb ('invoke'), resource ('Gemini CLI'), and distinguishes it from the sibling tool 'gemini_prompt' by mentioning the separate context block. However, it doesn't explicitly name the sibling for differentiation, keeping it at 4 rather than 5.

    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?

    The description provides some usage context: 'Stateless — each call is an independent turn' and 'Runs in the MCP server's current working directory.' It implies when to use this tool (for prompts with separate context blocks) but doesn't explicitly state when to use it versus the sibling 'gemini_prompt' or other alternatives, nor does it provide exclusions or prerequisites.

    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?

    No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: statelessness, independence of calls, and execution directory. However, it lacks details on permissions, rate limits, error handling, or output format. For a tool with no annotations, this is adequate but leaves gaps in 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 highly concise and front-loaded: three short sentences with zero waste. Each sentence adds value—stating the purpose, behavioral trait (statelessness), and execution context—earning its place efficiently.

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

    Completeness3/5

    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 2 parameters with full schema coverage, the description is minimally complete. It covers purpose and basic behavior but lacks details on output, errors, or advanced usage. For a stateless CLI tool, this is adequate but not comprehensive.

    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 description coverage is 100%, so the schema already documents both parameters ('prompt' and 'model') fully. The description doesn't add any parameter-specific meaning beyond what the schema provides, such as examples or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.

    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 states the tool's purpose: 'Invoke the headless Gemini CLI with a single prompt.' It specifies the verb ('invoke') and resource ('Gemini CLI'), and distinguishes it from the sibling tool by noting it's 'stateless — each call is an independent turn.' However, it doesn't explicitly name the sibling tool for full differentiation.

    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 for usage: 'Stateless — each call is an independent turn' and 'Runs in the MCP server's current working directory.' This implies when to use it (for independent prompts) and hints at an alternative (the sibling tool likely handles context). However, it doesn't explicitly name the sibling or state when-not-to-use scenarios.

    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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