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PierrunoYT

Replicate Ideogram V3 MCP Server

by PierrunoYT
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JavaScript
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Server Quality Checklist

50%
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: generate_image initiates an image generation request, while get_image_status checks the status of an existing request. There is no overlap in functionality, making it easy for an agent to select the correct tool.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (generate_image, get_image_status) with clear, descriptive names. The naming is uniform and predictable, using snake_case throughout.

    Tool Count2/5

    With only two tools, the server feels thin for its purpose of image generation via Ideogram V3. It lacks essential operations like retrieving generated images, managing requests (e.g., cancel), or handling variations, which limits functionality.

    Completeness2/5

    The toolset is severely incomplete for image generation workflows. While it covers initiating and checking status, there is no tool to retrieve the actual generated image, update requests, or handle errors, leaving agents unable to complete core tasks.

  • Average 2.9/5 across 2 of 2 tools scored.

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

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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?

    With no annotations provided, the description carries full burden but offers minimal behavioral context. It mentions the model (Ideogram V3 Balanced) and API (Replicate) but doesn't disclose rate limits, authentication needs, cost implications, output format, generation time, or error handling. The three modes are listed but not explained operationally.

    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 appropriately brief (two sentences) and front-loaded with the core purpose. Every sentence adds value: the first establishes the tool's function and context, the second enumerates capabilities. No wasted words or redundant information.

    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?

    For a complex 10-parameter image generation tool with no annotations and no output schema, the description is inadequate. It doesn't explain what the tool returns (image URL? binary data?), how to handle the output, error conditions, or important behavioral aspects like generation time or cost. The three modes are mentioned but not sufficiently contextualized.

    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%, providing comprehensive parameter documentation. The description adds minimal value beyond the schema, only implying that 'prompt' is for text-to-image and that 'image' and 'mask' relate to inpainting. It doesn't explain parameter interactions or provide usage examples.

    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 generates images using a specific model and API, and lists three supported modes (text-to-image, inpainting, style transfer). It distinguishes from the sibling 'get_image_status' by focusing on creation rather than status checking, though it doesn't explicitly contrast them.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description mentions three use cases (text-to-image, inpainting, style transfer) but provides no guidance on when to choose one over another, prerequisites for inpainting (requires mask), or alternatives. It doesn't explain when this tool should be used versus other image generation tools that might exist.

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

  • Behavior2/5

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

    With no annotations provided, the description carries full burden for behavioral disclosure. It states the action ('check status') but doesn't describe what statuses are possible, whether it's idempotent, rate limits, authentication needs, or response format. This is inadequate for a tool with zero annotation coverage.

    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 a single, efficient sentence with zero waste. It's appropriately sized for a simple tool and front-loaded with the core purpose, making it easy to parse quickly.

    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 no annotations and no output schema, the description is incomplete. It doesn't explain what status information is returned (e.g., pending, completed, failed), potential errors, or how it integrates with the sibling tool. For a status-checking tool, this leaves significant gaps in understanding its behavior and output.

    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 the schema fully documents the single parameter (prediction_id). The description adds no additional meaning beyond implying the parameter relates to an image generation request, which is already suggested by the tool name. Baseline 3 is appropriate when the schema does the heavy lifting.

    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's purpose as checking the status of an image generation request, using specific verbs ('check') and resources ('image generation request'). It distinguishes from the sibling tool 'generate_image' by focusing on status checking rather than generation, though it doesn't explicitly mention the sibling.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a prediction_id from generate_image), exclusions, or contextual cues for selection, leaving usage entirely implicit.

    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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