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GMKR

mcp-image-generator

by GMKR
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TypeScript
Hybrid

MCP 图像生成器

一个模型上下文协议 (MCP) 服务器，用于使用 Together AI 的图像生成模型生成图像。此 MCP 服务器可以在本地运行，也可以使用 SSE 端点运行。MCP 图像生成器需要一个提供程序，目前仅支持“Replicate”和“Together”。您需要设置TOGETHER_API_KEY或REPLICATE_API_TOKEN环境变量，并将PROVIDER环境变量设置为“replicate”或“together”。

SSE 端点（Docker 环境）

克隆存储库

git clone https://github.com/gmkr/mcp-imagegen.git
cd mcp-imagegen

构建并运行 Docker 容器

docker build -f Dockerfile.server -t mcp-imagegen .
docker run -p 3000:3000 mcp-imagegen

使用 MCP 客户端进行配置

{
  "mcpServers": {
    "imagegenerator": {
      "url": "http://localhost:3000/sse",
      "env": {
        "PROVIDER": "replicate",
        "REPLICATE_API_TOKEN": "your-replicate-api-token"
      }
    }
  }
}

将url调整为您要使用的 MCP 服务器的端点。 provider可以是“replicate”或“together”。

Related MCP server: pixel-surgeon-mcp

使用 stdio 在本地运行

先决条件

  • Node.js

  • Together AI API 密钥或复制 API 令牌

安装

  1. 克隆存储库：

    git clone https://github.com/gmkr/mcp-imagegen.git
cd mcp-imagegen

  2. 安装依赖项：

    pnpm install

配置

为您的 MCP 客户端创建一个配置文件。以下是示例配置：

{
  "mcpServers": {
    "imagegenerator": {
      "command": "pnpx",
      "args": [
        "-y",
        "tsx",
        "/path/to/mcp-imagegen/src/index.ts"
      ],
      "env": {
        "PROVIDER": "replicate",
        "REPLICATE_API_TOKEN": "your-replicate-api-token"
      }
    }
  }
}

将/path/to/mcp-imagegen替换为克隆存储库的绝对路径，并将your-replicate-api-token替换为实际的复制 API 令牌。

用法

MCP 图像生成器提供了一个名为generate_image的工具，可用于根据文本提示生成图像。

工具：generate_image

根据提供的提示生成图像。

参数：

  • prompt （字符串）：生成图像的文本提示

  • width （数字，可选）：要生成的图像的宽度（默认值：512）

  • height （数字，可选）：要生成的图像的高度（默认值：512）

  • numberOfImages （数字，可选）：要生成的图像数量（默认值：1）

环境变量

  • PROVIDER ：用于图像生成的提供程序（默认值：“replicate”）

  • REPLICATE_API_TOKEN ：您的复制 API 令牌

  • TOGETHER_API_KEY ：您的 Together AI API 密钥

  • MODEL_NAME ：用于图像生成的模型（默认值：“black-forest-labs/flux-schnell”）

执照

麻省理工学院

Available Tools

1 tool
generate_imageA

Generates and returns an image based on the provided promptUse this tool when you need to generate an image based on a promptThe image will be returned as a base64 encoded string

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesThe prompt to generate an image for
widthNoThe width of the image to generate
heightNoThe height of the image to generate
numberOfImagesNoThe number of images to generate

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that the image is 'returned as a base64 encoded string,' which adds useful behavioral context beyond the input schema. However, it lacks details on potential limitations (e.g., rate limits, quality constraints, or error conditions), leaving gaps for a mutation tool.

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 front-loaded with the purpose and usage guidelines in two sentences, with no wasted words. However, the lack of punctuation between sentences ('promptUse this tool') slightly reduces readability, preventing a perfect score of 5.

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 the tool's complexity (image generation with 4 parameters) and no annotations or output schema, the description is moderately complete. It covers the basic operation and output format but lacks details on behavioral traits (e.g., performance, errors) and does not explain return values beyond the base64 string, leaving room for improvement.

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%, meaning all parameters are documented in the schema itself. The description does not add any parameter-specific details beyond what the schema provides (e.g., format or constraints for 'prompt' or 'width'). Thus, it meets the baseline of 3 but does not enhance 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 tool's purpose: 'Generates and returns an image based on the provided prompt.' It specifies both the action (generate and return) and the resource (image). However, with no sibling tools provided, it cannot demonstrate differentiation from alternatives, which prevents a score of 5.

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 explicit guidance: 'Use this tool when you need to generate an image based on a prompt.' This clearly indicates the primary use case. However, it lacks exclusions or alternatives (e.g., when not to use it or other tools for similar tasks), which prevents a score of 5.

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. 1 tool update
    • First observedgenerate_image
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TDQS

A3.6/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'generate_image' has a clear and distinct purpose that cannot be confused with any other tool in this set.

Naming Consistency5/5

The single tool name 'generate_image' follows a clear verb_noun pattern. Since there is only one tool, naming consistency is inherently perfect with no deviations or mixed conventions to evaluate.

Tool Count2/5

A single tool is too few for a server named 'mcp-image-generator', which suggests a broader scope for image generation tasks. While the tool covers basic generation, the count feels thin and lacks operations like editing, upscaling, or managing generated images that might be expected.

Completeness2/5

The tool set is severely incomplete for an image generation domain. It only provides generation, with no coverage for common operations like editing images, adjusting parameters, retrieving generation history, or handling different formats, which will limit agent capabilities and cause workarounds.

Maintenance

ActivityInactive
ResponsivenessSyncing

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