MeiGen AI Image Generation MCP

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Server Quality Checklist

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Latest release: v1.3.3
Disambiguation5/5
Each tool targets a distinct task: workflow management, prompt enhancement, image generation, video generation, gallery inspiration, model listing, preferences, and gallery search. No significant overlap.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (e.g., enhance_prompt, generate_image, search_gallery), making it predictable for an agent.
Tool Count5/5
With 8 tools, the set covers the core image generation workflow without being overwhelming or sparse. Each tool serves a clear purpose.
Completeness4/5
Major operations are covered: generation, prompt enhancement, gallery search, workflow management, and preferences. Minor gaps exist (e.g., no explicit image editing or history), but the surface is largely complete.
Average 4.3/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 5 of 10 community issues answered or closed in the last 6 months
- 21 commits in the last 12 weeks
- Last stable release on August 5, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
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?
Annotations set readOnlyHint=false, implying mutation, and the description confirms 'Read or update'. No additional behavioral traits are disclosed (e.g., side effects, permissions, rate limits). The description adds minimal value beyond the annotation.
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?
Two sentences, front-loaded with purpose, no wasted words. Efficient and clear.
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?
The description lacks details about return values for each action, especially since no output schema is provided. It mentions one usage scenario (get at conversation start) but not the semantics of other actions. Overall adequate but with noticeable gaps.
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 each parameter is already documented. The tool description lists the preferences categories but does not add significant new meaning beyond the schema. Baseline 3 is appropriate.
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 reads or updates user preferences and lists specific preferences (style, aspect ratio, model, style notes, favorite prompts). It is a specific verb+resource combination that distinguishes it from sibling tools like generate_image or list_models.
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 advises calling with action 'get' at conversation start to load preferences. However, it does not provide guidance on when to use 'set', 'add_favorite', or 'remove_favorite', nor does it mention when not to use this tool or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide destructiveHint: true, so the description adds minimal behavioral context. It mentions delete action, which aligns, but no additional traits like persistence or side effects are disclosed.
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?
Single sentence with front-loaded actions. No wasted words. Efficient and clear.
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, no output schema, and rich sibling context, the description covers the gist well. Could mention return values for list/view, but overall adequate.
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 100% with detailed descriptions. The description adds marginal value, e.g., default save name for import. Baseline 3 is appropriate as the schema carries most semantic weight.
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 it manages ComfyUI workflow templates and lists specific actions (list, view, import, modify, delete). This verb+resource combination distinguishes it from siblings which are about prompt/image generation, models, preferences, etc.
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 implicitly identifies this as the sole tool for workflow management, but lacks explicit guidance on when to use vs alternatives or when not to use. It does provide parameter dependencies in schema descriptions, e.g., which actions require which parameters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true. The description adds the pricing URL and mentions capabilities but does not elaborate on behavioral traits beyond the read-only nature. No contradictions.
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?
Two concise sentences. Front-loaded with purpose, then a helpful link. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple tool (one optional param, read-only, no output schema), the description covers purpose and directs to pricing. It is complete for an agent to understand the tool's function.
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 100% with one parameter (activeOnly) already described. The description does not add further meaning beyond what the schema provides, so baseline 3 is appropriate.
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 'List available AI image generation models and their capabilities,' which is a specific verb-resource pair. It distinguishes from siblings like generate_image, generate_video, comfyui_workflow, etc., which are generation or enhancement tools.
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 implies usage for browsing models before generation, but it doesn't explicitly state when to use or not use it versus alternatives, nor does it provide exclusions. However, the purpose is straightforward and context is clear.
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?
Annotations already mark readOnlyHint=true; description adds value by detailing returned content (prompt, image URLs) and suggesting usage, without contradicting annotations.
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?
Three sentences, each adds value: purpose, usage, integration. No fluff, front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given simple tool with one parameter and no output schema, description fully explains what the tool returns and how to use it, making it self-contained.
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 covers 100% of parameter; description repeats schema description but adds no new semantic detail about the parameter itself.
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?
Description clearly states it retrieves full prompt and all image URLs for a gallery entry, and distinguishes from siblings by specifying how output integrates with generate_image() and style transfer.
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?
Implied usage after search_gallery; provides concrete guidance on how to use output (show images, use prompt and referenceImages). No explicit when-not-to-use, but context is clear.
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?
Annotations indicate this is a destructive operation (destructiveHint=true) and not read-only. The description elaborates on behaviors like automatic compression and upload of local files, and handling of reference images. It does not contradict annotations and adds useful context beyond the schema.
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, well-structured, and front-loaded with the main purpose. Every sentence provides useful information without redundancy. Tips and platform-specific notes are clearly separated and easy to parse.
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 the complexity (10 parameters, multiple providers, no output schema), the description covers the tool's behavior thoroughly, including provider-specific nuances and reference image handling. However, it does not describe the return format (e.g., whether the response includes URLs or base64), which would be helpful for an agent.
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?
All parameters have descriptions in the schema (100% coverage), so the description's role is to add extra context. It does so effectively, e.g., explaining that referenceImages can come from gallery URLs, local paths, or previous results, and clarifying the use of size vs. aspectRatio across providers. This adds value beyond the schema definitions.
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 'Generate an image using AI' and specifies the supported platforms (MeiGen, ComfyUI, OpenAI-compatible APIs). It distinguishes this tool from sibling tools like generate_video and enhance_prompt, establishing 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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides actionable guidance, such as obtaining prompts from get_inspiration() or enhance_prompt() and using gallery URLs as referenceImages. It also includes specific instructions for Midjourney V8.1 style codes. However, it does not explicitly state when NOT to use this tool or list alternatives.
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?
Annotations already indicate readOnlyHint=true. The description adds behavioral context by mentioning that results include image URLs and should be rendered as markdown images, which helps the agent understand the expected interaction. No contradiction with annotations.
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 very concise at three sentences, all of which are meaningful. The main purpose is front-loaded, followed by usage guidance and display instructions. No unnecessary words.
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?
For a search tool with no output schema, the description covers the main functionality, when to use, how to query (including empty query behavior), and how to display results. It lacks explicit pagination details, but given the schema's parameter descriptions, it is fairly 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?
Schema coverage is 100%, so baseline is 3. The description adds value by providing usage tips for the query parameter (e.g., natural language works well, leave empty to browse) and implies category usage. This exceeds the bare 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 clearly states the tool performs semantic search for AI image prompts, distinguishing itself from keyword searches. It also specifies that results include image URLs and how to render them, making the purpose very 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 states when to use the tool: for inspiration, exploring styles, or when users say 'generate an image' without a specific idea. While it doesn't explicitly list alternatives, the context and sibling tool names allow the agent to infer when not to use it.
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?
Annotations declare readOnlyHint=true, and the description adds 'Free, no API key needed,' providing cost and authentication transparency beyond what annotations offer. No behavioral contradictions are present.
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 sentences that convey purpose, usage, and key traits. Every sentence earns its place without unnecessary fluff.
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?
For a simple tool with two parameters, no output schema, and read-only behavior, the description covers purpose, usage, transparency, and parameter guidance. Minor missing detail: the output format of the enhanced prompt is not described, but it is not essential given the tool's simplicity.
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?
Although schema coverage is 100%, the description (especially the detailed guidance on the 'style' enum) adds significant contextual meaning, explaining when to use each style and how it affects prompt generation. This goes beyond what the schema alone provides.
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 function: 'Transform a simple idea into a professional image generation prompt.' This distinguishes it from siblings like generate_image (which generates images) and get_inspiration (which provides gallery ideas).
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 says 'Use when the user provides a brief description... and needs a detailed, high-quality prompt' and suggests combining with gallery inspiration. However, it does not mention when not to use or explicitly name sibling alternatives, leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (destructiveHint=true), the description details generation time, billing logic for reference video, output behavior (only new content, not concatenated), and model-specific fidelity tiers. No contradictions with annotations.
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 long but well-organized, front-loading the main purpose and then providing necessary details. Every sentence adds value, though it could be slightly more concise without losing clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (10 parameters, no output schema, minimal annotations), the description covers all modes, models, pricing, limitations, and expected output behavior comprehensively. It is complete for an AI agent to select and invoke the tool correctly.
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 coverage is 100%, yet the description adds substantial extra context: required phrase for prompt with reference video, model-specific allowed durations, resolution restrictions per tier, and billing implications for referenceVideo. It significantly enriches the 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 clearly states that the tool generates videos via AI on the MeiGen platform, supports multiple modes (text-to-video, image-to-video, reference-video continuation), and distinguishes itself from sibling tools like generate_image and list_models.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on when to use different models, pricing structures, prerequisites for reference video (prompt must include 'extend'/'continue'), and model-specific constraints (e.g., grok-video is image-to-video only). It gives clear usage conditions without relying on siblings.
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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