lemgen-ai-design-mcp

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes, but 'enhance_prompt' and 'prompt_tools' both deal with prompt improvement, which could cause confusion despite different specific actions.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with lowercase and underscores, e.g., 'generate_image', 'list_models', 'search_gallery'.
Tool Count5/5
8 tools is well-scoped for an AI design MCP, covering prompt enhancement, generation, inspiration, models, preferences, and gallery search without being excessive.
Completeness4/5
The tool set covers the core workflow of prompt creation, generation, and exploration, but lacks tools for managing generated assets (e.g., deletion or history), which is a minor gap.
Average 3.7/5 across 8 of 8 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 7 commits in the last 12 weeks
- No stable releases found
- 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.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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?
Annotations provide readOnlyHint=true, indicating a safe read operation. The description adds minor context about local availability and LemGen but does not disclose behavioral traits beyond annotations. No side effects or authentication needs are mentioned.
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 short with two sentences, front-loaded with the main purpose. It is efficient but the second sentence about LemGen could be seen as slightly tangential. Still, it is not verbose and avoids clutter.
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 4 parameters (3 enums) and no output schema, the description is too brief. It fails to explain the differences between actions, the role of targetLanguage, or expected results. An agent lacks sufficient context to correctly invoke the tool, especially without schema descriptions.
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?
The input schema has 0% description coverage, and the description does not compensate. It does not explain the four parameters, the meaning of each action, or the purpose of mediaType and targetLanguage. The schema's enums provide some structure, but the description adds no semantic value.
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: to translate, polish, or improve an AI image/video prompt. It uses specific verbs and identifies the resource (prompt), making the purpose clear. However, it does not explicitly distinguish from sibling tools like enhance_prompt, which may overlap.
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 mentions 'Free locally' and the optional LemGen prompt tool, providing some context on when to use this tool vs. an alternative. But it lacks explicit guidance on when to use each action (translate vs. improve vs. polish) and does not address sibling tools.
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?
Discloses slowness and the need for user confirmation, which are key behavioral traits. Annotations only provide readOnlyHint=false, so description adds value. However, it omits details like whether it consumes significant resources, cost implications, or rate limits.
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 the core purpose. Every sentence adds value with no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (11 params, no output schema, no parameter descriptions), the description is severely lacking. It covers only authentication and slowness, leaving the agent with insufficient context to use the tool correctly.
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?
With 11 parameters and 0% schema description coverage, the description must compensate but provides zero information about any parameter. User cannot infer what 'tier', 'model', 'duration', etc., mean from the description alone.
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 action ('Generate') and the resource ('AI video') with the specific provider 'LemGen'. It distinguishes from sibling tools like generate_image by specifying video generation. No ambiguity.
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?
Explicitly states that videos are slow and require explicit user confirmation, providing strong usage guidance. However, it does not mention when not to use this tool or suggest alternatives (e.g., generate_image for images).
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 indicate readOnlyHint=false, so the description's 'generate' is consistent. It adds the token requirement and usage timing, but does not disclose other behavioral traits like rate limits or effects of missing token, which is acceptable given the simple write action.
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 wastes no words on unnecessary details. Every sentence serves a clear function.
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 9 parameters, no output schema, and low schema description coverage, the description is too minimal. It does not explain key parameters, return format, or side effects, leaving significant gaps for an agent to correctly invoke the tool.
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?
Schema description coverage is only 11%, with most parameters having defaults but no descriptions. The tool description provides no additional information about parameters (e.g., quality, resolution, aspectRatio), leaving the agent uninformed about their meaning or appropriate values.
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 AI image' with LemGen, specifying the resource and action. It also provides context about requiring a token and using after user approval, which distinguishes it from siblings like generate_video or enhance_prompt.
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 includes when to use ('after user approves a final prompt') and a prerequisite ('requires LEMGEN_API_TOKEN'), offering clear context. However, it lacks explicit exclusions or alternatives for when not to use this tool compared to siblings.
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 indicate readOnlyHint=false, and the description clarifies it supports both reading and updating, which adds context. However, it does not disclose behavioral traits such as whether changes are persisted immediately, require additional steps, or affect other operations.
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, concisely stating the purpose and key attributes (free, local, no API key). Every word adds value with no redundancy.
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 7 parameters, no output schema, and no parameter descriptions, the description is insufficient for an agent to effectively invoke the tool. It lacks details on parameter value ranges, required combinations, and expected behavior for each action.
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?
With 0% schema description coverage, the description must compensate but only lists parameter names without explaining valid values, formats, or constraints. For example, 'style' and 'resolution' are mentioned but not what values they accept, leaving agents to guess.
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 explicitly states the tool's purpose: to read or update local LemGen preferences. It lists the specific preference fields (style, model, aspect ratio, resolution, favorite prompt ids), clearly indicating the resource and actions.
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 managing preferences but lacks explicit guidance on when to use this tool versus alternatives. No exclusions or when-not-to-use scenarios are mentioned, and sibling tools like 'prompt_tools' or 'list_models' are not differentiated.
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 valuable context ('Free, local, no API key required') beyond annotations, though it does not discuss output format or error behavior.
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 short sentences with no wasted words. The primary action is front-loaded, and the free/local note is appended efficiently.
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?
Missing explicit description of the return value (the enhanced prompt) and no clarification of which parameters are optional/required beyond the schema. Adequate but incomplete for a tool with 5 parameters and no output schema.
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?
With 60% schema coverage, the description does not add meaning beyond what the schema already provides (e.g., style options are listed in schema, not description). No compensation for missing parameter descriptions like referenceCount.
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 'Turn a short idea into a professional image/video prompt' clearly states the verb (enhance) and resource (prompt), distinguishing it from sibling tools like generate_image that create media directly.
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?
No explicit guidance on when to use this tool versus alternatives or when not to use it. The context implies use for prompt refinement before generation, but this is not stated.
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 read-only; description adds useful info about cost and authentication.
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 plus free/no-auth note; every word is necessary and front-loaded.
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?
Covers all returned items for a simple retrieval tool; no missing critical info.
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 'id' with description; description adds no extra 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?
Clearly states the tool retrieves full prompt, preview media, metadata, and source link for one gallery entry. Distinct from siblings like search_gallery.
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?
Mentions free/no API key but lacks explicit guidance on when to use vs alternatives like search_gallery.
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?
The description adds value beyond the readOnlyHint annotation by detailing what results include (preview URLs, LemGen source links) and access requirements (free, no API key). This provides useful behavioral context for selection.
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, front-loaded with the main action, and contains no extraneous information. Every phrase serves a purpose: specifying the resource, output, and access constraints.
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 tool's simplicity and the presence of annotations, the description is reasonably complete. It covers the resource, output format, and access. However, it omits details about pagination or result ordering (beyond what the sort parameter provides), which are minor gaps.
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?
The schema covers 50% of parameters with descriptions (query, category) but the description itself adds no parameter guidance. It does not elaborate on sort, limit, model, or mediaType beyond their schema definitions, leaving the agent to infer usage from parameter names and enums alone.
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 searches a curated AI image/video prompt library, with specific output including preview URLs and source links. It is distinct from sibling tools like generate_image or enhance_prompt, which are about creation or modification.
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 finding prompts via search, and mentions 'Free, no API key required' to encourage use. However, it does not explicitly contrast with get_inspiration or provide when-not scenarios, though the context is clear enough.
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?
Annotations already declare readOnlyHint=true. The description adds value by specifying it is free and requires no API key, and lists the exact information returned (strengths, ratios, resolutions, reference limits), going beyond 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 a single sentence that conveys all essential information without unnecessary words. It is well-structured and 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 the tool's simplicity (one optional parameter, no output schema, read-only), the description provides sufficient context: it lists what models are available and their attributes, and notes it is free. No key missing information.
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 0%, so the description should compensate. It mentions 'image and video models', hinting at the mediaType parameter, but does not explain the 'all' option or provide details on how to use the parameter. The enum values are self-explanatory, but more explicit guidance would help.
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 action (List), resource (LemGen models), and details (strengths, ratios, resolutions, reference limits). It distinguishes from sibling tools (generate_image, generate_video, etc.) by being a read-only listing tool.
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 use for checking available models before generation, noting it is free and requires no API key. However, it lacks explicit guidance on when not to use it or direct comparison to sibling tools like search_gallery.
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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