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MCP Server Replicate

MCP Server Replicate

by gerred

MCP 服务器复制

Python 版本执照 代码风格 类型检查器 拉夫 PyPI 版本 铁匠徽章

Replicate API 的 FastMCP 服务器实现,提供基于资源的 AI 模型推理访问,重点是图像生成。

特征

  • 🖼️基于资源的图像生成和管理

  • 🔄 通过订阅实时更新

  • 📝 模板驱动的参数配置

  • 🔍 全面的模型发现和选择

  • 🪝 用于外部通知的 Webhook 集成

  • 🎨 质量和样式预设,以获得最佳效果

  • 📊 进度跟踪和状态监控

  • 🔒 安全的 API 密钥管理

Related MCP server: MCP Server for Replicate

可用提示

服务器针对不同的任务提供了几个专门的提示:

文本转图像(主要)

我们最全面测试过、功能最强大的提示。针对从文本描述生成高质量图像进行了优化,具有以下功能:

  • 详细的样式控制

  • 质量预设(草稿、平衡、质量、极端)

  • 尺寸和纵横比自定义

  • 进度跟踪和实时更新

例子:

Create a photorealistic mountain landscape at sunset with snow-capped peaks, quality level: quality, style: photorealistic

其他提示

  • 图像到图像:转换现有图像(即将推出)

  • 模型选择:获得帮助,为您的任务选择合适的模型

  • 参数帮助:了解和配置模型参数

先决条件

安装

通过 Smithery 安装

要通过Smithery自动为 Claude Desktop 安装 MCP Server Replicate:

npx -y @smithery/cli install @gerred/mcp-server-replicate --client claude

手动安装

您可以直接从 PyPI 安装该包:

# Using UV (recommended)
uv pip install mcp-server-replicate

# Using UVX for isolated environments
uvx install mcp-server-replicate

# Using pip
pip install mcp-server-replicate

Claude 桌面集成

  1. 确保您安装了最新版本的 Claude Desktop

  2. 打开您的 Claude Desktop 配置:

# macOS
code ~/Library/Application\ Support/Claude/claude_desktop_config.json

# Windows
code %APPDATA%\Claude\claude_desktop_config.json
  1. 使用以下选项之一添加服务器配置:

{
  "globalShortcut": "Shift+Alt+A",
  "mcpServers": {
    "replicate": {
      "command": "uv",
      "args": ["tool", "run", "mcp-server-replicate"],
      "env": {
        "REPLICATE_API_TOKEN": "APITOKEN"
      },
      "cwd": "$PATH_TO_REPO"
    }
  }
}
  1. 设置您的复制 API 密钥:

# Option 1: Set in your environment
export REPLICATE_API_TOKEN=your_api_key_here

# Option 2: Create a .env file in your home directory
echo "REPLICATE_API_TOKEN=your_api_key_here" > ~/.env
  1. 完全重启Claude Desktop

您现在应该在 Claude Desktop 中看到 🔨 图标,表示 MCP 服务器可用。

用法

一旦连接到 Claude Desktop,您可以:

  1. 用自然语言生成图像:

    Create a photorealistic mountain landscape at sunset with snow-capped peaks
  2. 浏览你的世代:

    Show me my recent image generations
  3. 按世代搜索:

    Find my landscape generations
  4. 检查生成状态:

    What's the status of my last generation?

故障排除

服务器未显示在 Claude Desktop 中

  1. 检查 Claude Desktop 日志:

tail -n 20 -f ~/Library/Logs/Claude/mcp*.log
  1. 验证您的配置:

  • 确保claude_desktop_config.json中的路径是绝对路径

  • 确保 UV 已安装并位于您的 PATH 中

  • 检查您的复制 API 密钥是否已设置

  1. 尝试重启 Claude Desktop

有关更详细的故障排除,请参阅我们的调试指南。

文档

发展

  1. 克隆存储库:

git clone https://github.com/gerred/mcp-server-replicate.git
cd mcp-server-replicate
  1. 安装开发依赖项:

uv pip install --system ".[dev]"
  1. 安装预提交钩子:

pre-commit install
  1. 运行测试:

pytest

贡献

欢迎大家贡献!详情请参阅我们的贡献指南。

执照

该项目根据 MIT 许可证获得许可 - 有关详细信息,请参阅LICENSE文件。

Available Tools

18 tools
cancel_predictionC

Cancel a running prediction.

Inputs: prediction_id (required).

create_predictionC

Create a new prediction using a specific model version on Replicate.

    Args:
        input: Model input parameters including version or model details
        confirmed: Whether the user has explicitly confirmed the generation

    Returns:
        Prediction details if confirmed, or a confirmation request if not
    

Inputs: input (required), confirmed.

generate_imageC

Generate an image using the specified parameters.

Inputs: prompt (required), style, quality, width, +3 more.

get_collection_detailsC

Get detailed information about a specific collection.

Inputs: collection_slug (required).

get_model_detailsC

Get detailed information about a specific model.

    Args:
        model_id: Model identifier in format owner/name

    Returns:
        Detailed model information
    

Inputs: model_id (required).

get_predictionC

Get the status and results of a prediction.

Inputs: prediction_id (required), wait, max_retries.

get_webhook_secretA

Get the signing secret for verifying webhook requests.

Inputs: no named parameters.

list_collectionsB

List available model collections on Replicate.

Inputs: no named parameters.

list_hardwareB

List available hardware options for running models.

Inputs: no named parameters.

list_modelsC

List available models on Replicate with optional filtering by owner.

Inputs: owner.

list_templatesB

List all available templates with their schemas.

Inputs: no named parameters.

open_image_with_systemB

Open an image URL with the system's default application.

    Args:
        image_url: URL of the image to open

    Returns:
        Dict containing status of the operation
    

Inputs: image_url (required).

search_available_modelsB

Search for available models matching the query.

    Args:
        query: Search query describing the desired model
        style: Optional style to filter by

    Returns:
        List of matching models with scores
    

Inputs: query (required), style.

search_modelsC

Search for models using semantic search.

Inputs: query (required).

subscribe_to_generationD

Handle resource subscription requests.

Inputs: request (required).

unsubscribe_from_generationC

Handle resource unsubscribe requests.

Inputs: request (required).

validate_template_parametersC

Validate parameters against a template schema.

Inputs: input (required).

verify_webhookA

Verify that a webhook request came from Replicate using HMAC-SHA256.

    Args:
        payload: The webhook payload to verify
        signature: The signature from the X-Replicate-Signature header
        secret: The webhook signing secret from get_webhook_secret

    Returns:
        True if signature is valid, False otherwise
    

Inputs: payload (required), signature (required), secret (required).

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    C2.9/5.0

    Scored across 18 tools

    Disambiguation4/5

    Most tools have distinct purposes, but there is some overlap between 'search_available_models' and 'search_models' that could cause confusion, as both involve searching for models. The descriptions help differentiate them slightly, but the boundaries are not entirely clear.

    Naming Consistency5/5

    Tool names follow a consistent snake_case pattern with clear verb_noun structures throughout, such as 'create_prediction', 'get_model_details', and 'list_collections'. There are no deviations in naming conventions, making the set predictable and readable.

    Tool Count3/5

    With 18 tools, the count is borderline high for a server focused on Replicate's AI model platform. While it covers various aspects like predictions, models, and webhooks, it may feel slightly heavy and could benefit from consolidation to reduce complexity.

    Completeness4/5

    The tool set provides good coverage for the Replicate domain, including prediction lifecycle (create, get, cancel), model discovery (list, get, search), and webhook management. Minor gaps exist, such as missing tools for updating or deleting predictions or models, but agents can work around these with the available operations.

    Maintenance

    ActivityInactive
    ResponsivenessOutdated data

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