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2799662352

API易 MCP Server

by 2799662352
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JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one for single content generation, one for batch processing. No overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow the exact same prefix 'generate_content', with a descriptive suffix '_batch' for the batch variant. Perfectly consistent pattern.

    Tool Count4/5

    With only two tools, the server is minimal but scoped appropriately for Gemini content generation. Could potentially include a model listing tool, but not necessary.

    Completeness4/5

    The tool set covers the core functionality (single and batch generation). Missing optional features like model selection or streaming, but not gaps that prevent basic usage.

  • Average 4/5 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 status not available

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  • 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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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 must cover behavioral traits. It mentions concurrent execution and performance benefits, and shows a response format with success/failure per request. However, it does not disclose potential side effects, auth requirements, or rate limits.

    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?

    The description is well-structured with an example, front-loading the purpose and concurrency benefit. It is slightly lengthy but every sentence serves a purpose; the example is helpful.

    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?

    Given the absence of an output schema, the description includes a detailed response format example. It covers the `id` requirement and `max_concurrency` parameter, differentiating from `generate_content`. Some details like error handling beyond the response format are missing.

    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 baseline is 3. The description adds value by explaining the 'id' field requirement and showing an example, but does not significantly expand on other parameters beyond what the schema already provides.

    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 clearly states 'Generate multiple contents concurrently using Gemini', specifying the verb 'generate' and resource 'multiple contents'. It distinguishes from the sibling tool 'generate_content' by emphasizing concurrency and batch processing.

    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 explains that this tool is for multiple requests in parallel for better performance, implying its use case. However, it does not explicitly state when to use the single request alternative or provide exclusions.

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

  • Behavior4/5

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

    No annotations provided, so description carries full burden. It discloses supported file types, MIME auto-detection, model deprecation warnings, default values, and limits (max 10 files). However, output format and error handling are not mentioned, reducing transparency slightly.

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

    Conciseness3/5

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

    The description is lengthy with multiple examples, making it less concise. However, it is well-structured with sections and code blocks. Some information could be condensed without losing clarity.

    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?

    Lacks output schema or description of return values, and does not cover error handling or rate limits. For a tool with 9 parameters, the description is moderately complete but missing critical output context.

    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?

    Schema coverage is 100%, so baseline is 3. The description adds value by providing detailed model selection guidance (including deprecated models), example usage for parameters like files, enable_google_search, etc., clarifying how parameters interact.

    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 clearly states the tool generates content using Gemini, listing supported file types and capabilities. The sibling 'generate_content_batch' suggests this is for single generation, differentiating it effectively.

    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?

    Examples illustrate various use cases (with files, Google search, code execution, media resolution). Model selection guidance advises when to use each model. However, it does not explicitly contrast with the batch sibling, and assumes context from the name.

    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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