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Doctor-Pan-code

image-gen-mcp

by Doctor-Pan-code
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one lists available image models, the other generates images. No overlap or confusion possible.

    Naming Consistency5/5

    Both tools use a consistent verb_noun pattern in snake_case: 'list_image_models' and 'generate_image', making them predictable and easy to understand.

    Tool Count3/5

    With only 2 tools, the server feels under-scoped for its stated support of multiple providers and model selection. While minimal, it may be acceptable for a focused image generation task, but lacks coverage for related operations.

    Completeness3/5

    The server covers basic listing and generation, but lacks tools for retrieving generated images (URLs expire), managing providers, or handling model customization beyond the generate call. Minor gaps exist that could limit agent autonomy.

  • Average 3.8/5 across 2 of 2 tools scored. Lowest: 3.2/5.

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

    • No community issues in the last 6 months
    • 1 commit 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

  • 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

  • Behavior2/5

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

    No annotations exist, so the description must fully cover behavioral traits. It only states the tool lists models and instructions, with no mention of auth, rate limits, or read-only nature.

    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 clear sentence, front-loaded with the action and resource. No redundancy, though it could be expanded slightly.

    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 zero parameters and an output schema, the description is adequate but could specify the source of the data (e.g., provider-specific) or the format of usage instructions.

    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?

    There are zero parameters, so the schema provides no information. The description adds meaning by indicating the output includes model list and usage instructions, but it is minimal.

    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 lists image generation models and usage instructions recommended by the current provider. It distinguishes from sibling generate_image by focusing on listing rather than generation.

    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?

    Usage is implied but not explicit. It is reasonable to use this before generate_image, but no explicit when-to-use or alternative guidance is provided.

    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. Discloses multi-provider support, default model behavior, size constraints, and URL expiration. Does not mention rate limits or authorization, but covers 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?

    Well-structured with bullet points and sections. Information is valuable but slightly lengthy; some repetition could be trimmed. Good front-loading of purpose.

    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 output schema exists, return values need no explanation. Covers generation behavior, provider options, parameter defaults, and URL expiry. Could explicitly mention sibling tool for model list, but overall very complete.

    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?

    Despite 0% schema coverage signal, description explains 'model' (defaults per provider, custom) and 'size' (recommended dimensions) in detail. 'n' is implied but not explicitly explained; 'prompt' is clarified as text description. Adds significant meaning beyond schema.

    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 images from text descriptions, using verb 'generate' and resource 'image(s)'. It distinguishes itself from the sibling tool 'list_image_models' by being about generation, not listing.

    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 specific use cases (illustrations, posters, etc.), default models per provider, and size recommendations. Lacks explicit 'when not to use' or direct alternatives, but the sibling tool context helps.

    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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