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Paipai112

image2-mcp

by Paipai112
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: generating images and listing generated images. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern: 'generate_image' and 'list_images'. The convention is uniform and predictable.

    Tool Count3/5

    With only 2 tools, the server feels thin for its domain, but it covers the essential generation and listing operations. It is borderline reasonable but lacks additional supporting tools.

    Completeness4/5

    The server covers core image generation and listing. However, it lacks individual image retrieval, deletion, or progress tracking for async operations, which are minor but notable gaps.

  • 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
    • 19 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.

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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?

    With no annotations provided, the description carries the full burden. It states the tool lists recent images but does not disclose details like sorting order, recency definition, or whether it returns metadata. The behavior is minimally described but not fully transparent.

    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 extremely concise—two short sentences with no extraneous information. Every word serves a purpose: stating the action and providing usage context.

    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?

    Despite having an output schema, the description lacks parameter explanations and a definition of 'recently.' For a simple listing tool, it is adequate but not comprehensive; agents may need to infer or experiment.

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

    Parameters2/5

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

    Schema description coverage is 0%, yet the description offers no explanation for the two parameters ('limit' and 'output_dir'). Agents receive no guidance on how to use these parameters, which is a significant gap.

    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's purpose: 'List recently generated images in the output directory.' The verb 'List' and resource 'recently generated images' are specific, and the sibling tool 'generate_image' contrasts as a creation tool, making the distinction clear.

    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 explicitly mentions usefulness 'after async/fire-and-forget generation to see what's been created,' providing clear context for when to use this tool. However, it lacks explicit when-not-to-use or alternative suggestions beyond the implied sibling.

    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?

    With no annotations, the description discloses key behaviors: async mode behavior, size limits and ratio rules. Does not mention failure handling or auth, but covers the main behavioral traits.

    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?

    Two sentences, front-loaded with purpose. Second sentence packs size details efficiently. No superfluous text.

    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?

    With an output schema present, description focuses on behavioral aspects. Covers async vs sync and size constraints, which are critical. Missing details on quality and error handling, but adequate for generation tool.

    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 coverage is 0%, so description must compensate. Explains async_mode and size in detail, but does not cover 'quality,' 'filename,' or 'output_dir' parameters. Adds partial value but leaves gaps.

    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?

    Clearly states 'Generate an image from a text prompt using the company image2 model,' specifying the action and model. Distinguishes from sibling 'list_images' which lists existing images.

    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?

    Provides clear context for async mode usage (fire-and-forget vs wait) and size constraints. No explicit when-not or alternatives beyond the sibling, but sufficient for the tool's domain.

    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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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.

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