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rbibbey

agileimagegen-mcp

by rbibbey
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TypeScript
Local

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, image.generate and image.edit, are clearly distinct: one creates a new image and the other modifies existing images. There is no overlap in their purposes, making it easy for an agent to select the correct tool.

    Naming Consistency5/5

    Both tool names follow a consistent image.<verb> pattern (generate, edit). The uniform use of the dot separator and the shared namespace creates a predictable naming convention.

    Tool Count3/5

    With only 2 tools, the server feels thin. While both tools serve a clear purpose, the count is borderline and does not provide a broader toolkit that might be expected from an image-focused server.

    Completeness4/5

    The server covers the core operations of generating and editing images, which are its stated purpose. There are no obvious missing operations within that narrow scope, though it lacks any lifecycle management (e.g., listing or deleting images).

  • Average 3.5/5 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.

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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 disclose behavior. It mentions saving results to disk and returning structured metadata, which is more than absent, but it does not disclose whether original files are overwritten, permission requirements, reversibility, or any other side effects beyond saving. This is a moderate level of transparency.

    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 two sentences long, front-loaded with the main purpose, and every word contributes to understanding the tool. There is no verbosity or repetition.

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

    Completeness2/5

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

    Despite having an output schema, the tool has 7 parameters, 2 enum-based, and 2 required. The description does not cover the semantics of these parameters, nor does it mention constraints or usage context. It is too incomplete for a tool of this complexity.

    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%, so the description must explain parameter meanings. It only implicitly refers to prompt and input_image_paths ('Edit one or more local images', 'multimodal input'). No meaning is added for model, output_dir, filename_hint, transparency_mode, or transparency_threshold, leaving most of the 7-parameter schema unexplained.

    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 edits one or more local images using Gemini with multimodal input, and specifies outcomes (saves to disk, returns metadata). The verb 'edit' distinguishes it from the sibling 'image.generate'.

    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 by the description—use for editing existing local images—but there is no explicit guidance on when to prefer this over alternatives, no prerequisites mentioned (e.g., file existence), and no exclusions or alternative tool references.

    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?

    Since no annotations are provided, the description carries the full burden of disclosing side effects. It transparently states that the tool requires a Google AI Studio API key, saves the result to disk, and returns structured metadata. However, it does not mention potential overwrite behavior, network usage, or error conditions, which would enhance transparency.

    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 concise at two sentences, front-loaded with the primary action 'Generate an image', and provides key behavioral context (API key, disk save, metadata) without unnecessary detail.

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

    Completeness2/5

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

    Given the tool has 11 parameters, no annotations, and only a minimal description, the context is incomplete. While an output schema exists to explain return values, the description lacks parameter semantics, usage guidance, and deeper behavioral specifics, making it insufficient for a tool of this complexity.

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

    Parameters1/5

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

    The input schema has 11 parameters with 0% description coverage, and the tool description does not explain any of them. The description only mentions generating an image and saving to disk, leaving all parameter details ambiguous. This is a significant gap that the description fails to compensate for.

    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 an image with Gemini, distinguishing it from the sibling image.edit by using the verb 'Generate' versus 'edit'. It also mentions saving to disk and returning metadata, which gives a clear purpose.

    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 implies usage for generating new images, but it does not explicitly contrast with the sibling image.edit or provide when-not-to-use guidance. It mentions an API key requirement, which is helpful context, but lacks explicit alternative selection criteria.

    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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