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alarconcesar

flow-mcp

by alarconcesar
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Python
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Server Quality Checklist

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

  • Latest release: v0.2.0

  • Disambiguation5/5

    With only one tool, there is no possibility for confusion or overlap between tools. The single tool has a clear, distinct purpose.

    Naming Consistency5/5

    The sole tool uses a consistent verb_noun pattern (generate_image). With only one tool, naming consistency is inherently maintained.

    Tool Count3/5

    The server has only one tool, which is on the low end of reasonable scope. While the tool itself is feature-rich, a single tool may feel insufficient for a full image generation service.

    Completeness4/5

    The tool covers a wide range of image generation capabilities (text-to-image, image-to-image, upscaling, multiple models and resolutions). Minor gaps like model listing or configuration retrieval are absent but not critical for core functionality.

  • Average 4.4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 13 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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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

  • Behavior4/5

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

    With no annotations provided, the description carries the full burden. It discloses that it uses saved authentication, bypasses chat quotas, and supports upscale. It notes the 'Ultra' requirement for 4K. However, it omits rate limits, costs, and error behavior, preventing a higher score.

    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 a single dense paragraph, but it is well-structured and front-loaded with the core purpose. It packs significant detail without extraneous words. Could be slightly improved by using bullet points for readability, but it remains concise and informative.

    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 6 parameters (1 required), 2 enums, and an output schema (true), the description covers the essential functionality, parameter options, and quirks like the Ultra requirement. It lacks details on return values, but the output schema likely covers that. Minor gaps in error handling or batch limitations prevent a perfect score.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, so the description must compensate. It adds substantial meaning: explains `count` as number of images, lists model options with defaults, interprets `aspect` ratios, clarifies `resolution` defaults and options (1k, 2k, 4k), and describes `reference_image` for image-to-image. This fully compensates for the lack of schema descriptions.

    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 explicitly states it generates images via Google Flow's batchGenerateImages API, lists supported modes (text-to-image and image-to-image), and details models, aspects, and resolutions. It clearly distinguishes itself from the Flow Agent chat quota by noting it bypasses that limitation, making the purpose unmistakable.

    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 usage guidance, such as bypassing the chat quota and requiring 'Ultra' for 4K. It details options without ambiguity. However, it does not explicitly state when not to use this tool or mention alternatives, though no sibling tools exist. Slight room for improvement.

    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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