LemGen AI Design MCP

Server Quality Checklist

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

Latest release: v0.1.0
Disambiguation3/5
Most tools are distinct, but 'enhance_prompt' and 'prompt_tools' both improve prompts, creating potential confusion. Descriptions help somewhat but overlap remains.
Naming Consistency4/5
Seven of eight tools follow a clear verb_noun pattern. The exception is 'prompt_tools' (noun_noun), a minor deviation from the otherwise consistent naming.
Tool Count5/5
With 8 tools covering prompt enhancement, generation, gallery search, inspiration, model listing, and preferences, the count is well-scoped for a design server.
Completeness4/5
Core workflows (prompt creation, generation, exploration) are covered. Missing tools for deleting generated media or managing a user's own library, but these are plausible gaps.
Average 3.7/5 across 8 of 8 tools scored. Lowest: 3.1/5.
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 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.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?
Discloses requirement for LEMGEN_API_TOKEN, which is useful beyond readOnlyHint=false annotation. Lacks details on side effects, rate limits, or what happens with parameters like wait.
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, front-loaded sentences. Every word serves a purpose 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 9 parameters and no output schema, the description is too brief. It does not explain return values, error states, or how parameters like wait and resolution affect 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?
Description adds no information about any of the 9 parameters. With only 11% schema description coverage, the description fails to compensate, leaving parameter purpose unclear.
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?
Clearly states the verb 'generate' and resource 'AI image', with the specific model 'LemGen'. Differentiates from siblings like generate_video. Could be more explicit about its unique capabilities.
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?
Provides a clear usage condition: 'Use after the user approves a final prompt.' However, does not mention when not to use or suggest alternatives among 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?
Annotations already declare readOnlyHint=true, so the description adds minimal behavioral context beyond stating it modifies prompts (which is consistent). It does not detail the return format or any potential side effects, but the read-only annotation covers the safety aspect.
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?
Two sentences: the first clearly states the core purpose, the second adds value by noting local vs. remote availability. No wasted words, though the second sentence could be clearer.
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?
The description covers core functionality but omits essential details like return value format and differentiation from sibling tools. With no output schema, the agent has no guidance on what to expect from the tool, leaving it incomplete.
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 mentions 'action' in the verb list and 'mediaType' implied by 'image/video'. The 'targetLanguage' parameter is not explained, and the difference between 'polish' and 'improve' is unclear.
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 translates, polishes, or improves an AI image/video prompt, specifying the verb and resource. However, it does not differentiate it from the sibling tool 'enhance_prompt', leaving ambiguity about when to use each.
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 provides some usage context by noting that the tool is free locally and mentions an alternative (LemGen) when an API token is configured. But it does not explicitly guide when to use this tool versus other siblings like 'enhance_prompt' or 'generate_image'.
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?
Matches annotation (not read-only). Adds context that it's local and requires no API key, which is useful safety info. Does not detail effects of updates or persistence.
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 core purpose, then helpful context. Every word earns its place.
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 7 params, no output schema, and annotations not read-only, description provides reasonable overview but lacks detail on actions, parameter specifics, and return values.
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 coverage, description only mentions high-level fields but doesn't explain each parameter's purpose or constraints. Action enum values not clarified. Partial but insufficient.
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 reads/updates local preferences and lists specific fields (style, model, aspect ratio, etc.). Distinct from sibling tools which are generation-oriented.
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?
Tells when to use (for preferences) and mentions it's free/local, but lacks explicit exclusions or alternatives. Adequate but not strong.
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 readOnlyHint false, confirming a write operation. The description adds behavioral context: the tool requires an API token and is slow, which is valuable beyond the annotations. It does not detail other traits like rate limits or side effects.
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, consisting of two short sentences that each serve a clear purpose: stating the tool's function and providing a critical usage guideline. No extraneous content.
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 the tool's complexity (11 parameters, no output schema), the description offers minimal context beyond basic purpose and a usage warning. It lacks explanations of parameters, return values, authentication specifics, or behavior under different conditions, making it incomplete for effective use.
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 description provides zero information about the 11 parameters; they are only defined in the schema with 0% coverage. The token requirement is mentioned but not as a parameter. The description fails to help the agent understand parameter meaning or relationships.
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 AI video using LemGen, distinguishing it from sibling tools like generate_image. The verb 'generate' and resource 'video' are specific and unambiguous.
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 warns that videos are slow and should only be called after explicit user confirmation, providing clear context for when to use the tool. It does not explicitly mention alternative tools for quick tasks, but the guidance is sufficient.
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. The description adds 'Free, local, no API key required,' which aligns with no destructive or external state changes, and provides additional context about cost and privacy. It does not contradict annotations and adds value beyond them.
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 and front-loaded, consisting of two short sentences that convey the purpose and key features. Every word earns its place with no redundancy.
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?
No output schema is provided, but the description implies the output is a professional prompt. Given the simplicity of the tool and that it only enhances prompts, the description is reasonably complete. It could benefit from mentioning the format or structure of the enhanced prompt, but the current version is 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?
The input schema has 5 parameters with 60% schema description coverage. The description does not add any parameter information beyond what the schema already provides. For covered parameters, the schema is sufficient, so a baseline score of 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's function with a specific verb 'turn' and resource 'short idea into a professional image/video prompt'. It distinguishes itself from sibling tools like generate_image and generate_video, which actually generate media, while this enhances prompts.
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 highlights that it is 'Free, local, no API key required,' which hints at when to use (privacy, no cost). However, it does not explicitly state when not to use or compare with alternatives like get_inspiration or prompt_tools. Implied but not explicit.
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, confirming a safe read operation. The description adds valuable behavioral context by detailing the returned content (prompt, media, metadata, source link) and stating it's free with no authentication needed. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences with no redundant information. It front-loads the primary action and key details, making every sentence purposeful.
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 one-parameter retrieval tool, the description adequately covers what is returned and the free access. It does not explain error handling or rate limits, but given the tool's simplicity and the presence of annotations, it is sufficiently complete for an agent to use correctly.
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% with a single 'id' parameter described as 'Prompt id from search_gallery.' The description does not add further meaning beyond the schema, so it meets the baseline 3 for high coverage.
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 retrieves 'full prompt, preview media, metadata, and LemGen source link for one gallery entry,' specifying the exact data returned. It distinguishes itself from siblings like 'search_gallery' which lists entries rather than fetching details.
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 it's free and requires no API key, implying low usage barriers. However, it does not explicitly advise when to use this tool versus alternatives like 'search_gallery' or provide exclusions. The usage context is implied but not fully articulated.
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 declare readOnlyHint=true, confirming no destructive actions. The description adds that results include preview URLs and LemGen source links, plus free access with no API key, providing useful behavioral context 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 with no extraneous words. The first sentence clearly states the core purpose, and the second adds result details and a key feature (free, no API key). Every sentence earns its place.
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 covers basic purpose and output, but for a search tool with 6 optional parameters and no output schema, it lacks details on how sorting, filtering, and pagination work. This leaves the agent needing to infer or experiment with defaults.
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 coverage is 50%; only 3 of 6 parameters have descriptions in the schema. The tool description does not add any information about how parameters (sort, limit, model, query, category, mediaType) affect behavior, leaving gaps for half the input 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 searches the LemGen curated AI image/video prompt library, a specific verb and resource. It distinguishes from sibling tools like generate_image and generate_video which create new content, and enhance_prompt which modifies prompts.
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 mentions 'Free, no API key required', indicating ease of access and no authentication barrier. However, it does not explicitly compare to sibling tools or state when not to use it, leaving the selection partially to inference.
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 useful behavioral info beyond the readOnlyHint annotation, such as being free and requiring no API key, and specifies the returned information (strengths, ratios, resolutions, reference limits). 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 sentences concisely state the purpose and an important usage detail (free, no API key). No superfluous content.
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 read-only listing tool with one parameter and no output schema, the description adequately covers what it does and returns. It does not mention pagination or limits, but that is likely acceptable for this tool.
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 description coverage is 0%, but the description clarifies that the mediaType parameter filters by image/video/all, adding meaning beyond the enum values in the schema. The single parameter's purpose is well explained.
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 lists LemGen models with specific attributes (strengths, ratios, resolutions, reference limits). It distinguishes from sibling tools which are about generation, enhancement, and search.
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 listing models but does not explicitly state when to use this tool versus alternatives or when not to use it. The context of sibling tools makes it the only list tool, but explicit guidance is lacking.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![lemgen-ai-design-mcp MCP server](https://glama.ai/mcp/servers/gptimage2prompts/lemgen-ai-design-mcp/badges/card.svg)](https://glama.ai/mcp/servers/gptimage2prompts/lemgen-ai-design-mcp)

Score Badge

Copy to your README.md:

[![lemgen-ai-design-mcp MCP server](https://glama.ai/mcp/servers/gptimage2prompts/lemgen-ai-design-mcp/badges/score.svg)](https://glama.ai/mcp/servers/gptimage2prompts/lemgen-ai-design-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/gptimage2prompts/lemgen-ai-design-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server