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Letz-AI

Letz AI MCP

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LetzAI MCP Setup Guide

This guide will walk you through the process of setting up and using the LetzAI MCP (Model Context Protocol) for image generation.

Prerequisites

Before you begin, ensure that you have the following:

Related MCP server: iRAG MCP Server

Setup Steps

1. Download the Git Folder

Download the repository containing the LetzAI MCP project and place it in a location outside of your Downloads folder. For example:

C:\\Users\\username\\desktop

Alternatively, you can use git clone to clone the repository:

git clone <repository-url> C:\\Users\\username\\desktop

2. Install Dependencies

Navigate to the project folder using your terminal or command prompt:

cd C:\\Users\\username\\desktop

Run the following command to install all required dependencies:

npm install

3. Compile the Project

After installing the dependencies, compile the TypeScript files into JavaScript using the following command:

npx tsc

This will generate the compiled JavaScript files in the build folder.

4. Restart Claude App

After running npx tsc, you must restart the Claude Desktop App for it to recognize the updated MCP configuration and compiled files.

5. Set Up MCP Configuration in Claude Desktop App

open settings

  1. Open the Claude Desktop App.

  2. Click on the Menu Icon in the top-left corner.

  3. From the dropdown, select File.

  4. Navigate to Settings.

  5. Under the Developer section, you will see an option for Edit Config. view developer settings

  6. Click on Edit Config — this will open the configuration folder.

  7. Locate the file claude_desktop_config.json and edit it as needed.

Windows Configuration:

{
  "mcpServers": {
    "letzai": {
      "command": "node",
      "args": [
        "C:\\ABSOLUTE\\PATH\\TO\\PARENT\\FOLDER\\letzai-mcp\\build\\index.js"
      ],
      "env": {
        "LETZAI_API_KEY": "<Your LetzAI API Key>"
      }
    }
  }
}

Ubuntu Configuration:

{
  "mcpServers": {
    "letzai": {
      "command": "node",
      "args": ["/ABSOLUTE/PATH/TO/PARENT/FOLDER/letzai-mcp/build/index.js"],
      "env": {
        "LETZAI_API_KEY": "<Your LetzAI API Key>"
      }
    }
  }
}

macOS Configuration:

{
  "mcpServers": {
    "letzai": {
      "command": "node",
      "args": ["/ABSOLUTE/PATH/TO/PARENT/FOLDER/letzai-mcp/build/index.js"],
      "env": {
        "LETZAI_API_KEY": "<Your LetzAI API Key>"
      }
    }
  }
}

Configuration Explanation

  • command: The command to run the application. We use node to run the JavaScript file generated by TypeScript.

  • args: This is the path to the compiled index.js file. Make sure the path is correct according to where your files are located after compilation. If you've placed the folder at C:\\Users\\username\\desktop\\letzai-mcp, the path will be:


C:\\Users\\username\\desktop\\letzai-mcp\\build\\index.js

6. Run the MCP Server

Now that everything is set up, you can start using the LetzAI MCP in the Claude Desktop App. The server should be ready for image generation tasks once the app is running with the correct API key in the environment.

Important: After making changes to the configuration, you must restart Claude for the changes to take effect.

7. Testing the New MCP in Claude

claude prompt ui after installtion Click on the hammer icon to view the installed MCP tools. claude mcp tools

Once you've set up the MCP in the Claude Desktop App, you can test it by running the following prompt:

  • Create image with LetzAI using prompt: "photo of @mischstrotz drinking a beer, dressed as a knight"

This will create the image based on the provided prompt, using the model @mischstrotz from LetzAI. Claude will open the image in your preferred browser.

This will upscale the image using the strength parameter 1. You can pass entire URLs, or just the LetzAI Image IDs e.g. d6a67077-f156-46d7-a1a2-1dc49e83dd91

Troubleshooting

  • Node.js not found: Ensure that Node.js is installed and added to your system's PATH environment variable.

  • Invalid API Key: Double-check that you have correctly added your API key under the LETZAI_API_KEY variable in the Claude Desktop App settings.

  • File Path Issues: Make sure that the path to the index.js file is correct. If you're unsure about the path, use the absolute path to the file.

For more detailed documentation and support, visit LetzAI Docs.

Available Tools

2 tools
letzai_create_imageC

Create an image using the LetzAI public api

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesImage prompt to generate a new image. Can also include @tag to generate an image using a model from the LetzAi Platform
widthNoWidth of the image should be between 520 and 2160 max pixels. Default is 1600.
heightNoHeight of the image should be between 520 and 2160 max pixels. Default is 1600.
qualityNoDefines how many steps the generation should take. Higher is slower, but generally better quality. Min: 1, Default: 2, Max: 5
creativityNoDefines how strictly the prompt should be respected. Higher Creativity makes the images more artificial. Lower makes it more photorealistic. Min: 1, Default: 2, Max: 5
hasWatermarkNoDefines whether to set a watermark or not. Default is true
systemVersionNoAllowed values: 2, 3. UseLetzAI V2, or V3 (newest).
modeNoSelect one of the different modes that offer different generation settings. Allowed values: default, sigma, turbo. Default is slow but high quality. Sigma is faster and great for close ups. Turbo is fastest, but lower quality.turbo

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure but only states the basic action. It doesn't cover authentication needs, rate limits, response format, error handling, or any side effects (e.g., whether creation is idempotent or has costs). This leaves significant gaps for an AI agent to understand operational 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?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse while avoiding redundancy or fluff.

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 complexity of an 8-parameter image generation tool with no annotations and no output schema, the description is insufficient. It lacks details on return values, error conditions, usage constraints, and how it integrates with the sibling tool, leaving the agent with incomplete operational context.

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 schema description coverage is 100%, providing detailed documentation for all 8 parameters. The description adds no additional parameter semantics beyond what's already in the schema, so it meets the baseline score of 3 without compensating or enhancing parameter understanding.

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 action ('create an image') and the target resource ('using the LetzAI public api'), making the purpose immediately understandable. It distinguishes from the sibling tool 'letzai_upscale_image' by focusing on generation rather than enhancement, 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 provides no guidance on when to use this tool versus alternatives or any contextual prerequisites. It mentions the LetzAI public API but doesn't specify use cases, limitations, or when to choose this over other image generation tools.

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

letzai_upscale_imageC

Upscale an image using the LetzAI public api

ParametersJSON Schema
NameRequiredDescriptionDefault
imageIdNoThe unique identifier of the image to be upscaled.
imageUrlNoThe URL of the image to be upscaled. Must be a publicly available URL.
strengthYesThe strength of the upscaling process. Min. 1, Max. 3.

TDQS

C2.9/5.0
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 mentions using a public API but doesn't disclose critical traits like authentication requirements, rate limits, cost implications, error handling, or what happens to the original image. For a tool that modifies content with no annotation coverage, this leaves significant gaps in understanding its 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?

The description is a single, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized for a straightforward tool and front-loads the essential information. Every word earns its place, making it maximally concise.

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 modifies images (implied mutation), has no annotations, and no output schema, the description is incomplete. It doesn't explain what 'upscale' means practically, what format/resolution results are expected, whether the operation is reversible, or what happens if both imageId and imageUrl are provided. For a 3-parameter tool with no structured safety or output information, more context is needed.

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 already documents all three parameters thoroughly. The description adds no additional meaning about parameters beyond what's in the schema. It doesn't explain the relationship between imageId and imageUrl, or provide context about strength values. This meets the baseline for high schema coverage.

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 action ('Upscale') and resource ('an image') using the LetzAI public API. It distinguishes from the sibling tool 'letzai_create_image' by focusing on upscaling existing images rather than creating new ones. However, it doesn't specify the exact upscaling method or output characteristics, keeping it at a 4 rather than a 5.

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, limitations, or comparison with the sibling 'letzai_create_image' tool. The agent must infer usage from the tool name and parameters alone, which is insufficient for clear decision-making.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 2 tool updates
    • First observedletzai_create_image
    • First observedletzai_upscale_image
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TDQS

B3.1/5.0
Disambiguation5/5

The two tools have completely distinct purposes: one creates images from scratch, while the other upscales existing images. There is no overlap in functionality, and an agent can easily differentiate between them based on their clear descriptions.

Naming Consistency5/5

Both tools follow a consistent 'letzai_verb_noun' pattern with snake_case, using 'create_image' and 'upscale_image' as the core naming structure. This makes them predictable and easy to parse for an agent.

Tool Count2/5

With only two tools, the server feels thin for an AI image generation domain. While create and upscale are core operations, notable gaps like editing, inpainting, or style transfer are missing, making the toolset under-scoped for typical image manipulation workflows.

Completeness2/5

The server covers basic image creation and upscaling but lacks essential operations for a complete image generation surface. There are no tools for editing, modifying, or deleting images, and advanced features like batch processing or style application are absent, leading to potential dead ends for agents.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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