open-index/open-npm huggingface dataset:
npm package: @iflow-mcp/cappt-team-mcp-server
Description: A MCP server for Cappt
Author: ye4241
Latest version: 0.1.5

# mcp-server-cappt: 咔片MCP服务器

<div align="center">
  <img alt="Cappt" height="128" width="128" src="https://raw.githubusercontent.com/cappt-team/mcp-server-nodejs/main/assets/logo.png"/>
</div>

## 概述

一个MCP（Model Context Protocol）服务器，用于生成咔片（cappt.cc）的提纲和演示文稿。

### 工具

1. `generate_presentation`

- 从提纲生成演示文稿（由 `generate_outline` 生成）
- 输入：
  - `outline`（字符串）：标准提纲
  - `include_gallery`（布尔值）：是否在结果中包含图库
  - `include_preview`（布尔值）：是否在结果中包含预览
- 返回：
  - `record_id`：生成的演示文稿的记录 ID
  - `status`：生成过程的状态
  - `total_page`：演示文稿的总页数
  - `editor_url`：编辑演示文稿的 URL
  - `title`：演示文稿的标题
  - `thumbnail`：演示文稿的缩略图
  - `gallery`：演示文稿的图库
  - `preview`：演示文稿的预览

### 提示词

1. `generate_outline`

- 生成标准提纲
- 输入：
  - `input`（字符串）：用户输入，例如标题、文章等
- 返回：标准提纲

## 安装

```
cd path/to/repo
npm install
npm run build
```

## 配置

### 与 Claude Desktop 一起使用

将以下内容添加到你的 `claude_desktop_config.json` 中：

```json
{
  "mcpServers": {
    "sentry": {
      "command": "node",
      "args": [
        "path/to/repo/dist/index.js"
      ],
      "env": {
        "CAPPT_TOKEN": "YOUR_CAPPT_TOKEN"
      }
    }
  }
}
```

## 调试

你可以使用 MCP 检查器来调试服务器。请确保你已使用 `uv` 安装此软件包，然后运行：

```
npx @modelcontextprotocol/inspector node dist/index.ts
```

## 许可证

此 MCP 服务器根据 MIT 许可证授权。这意味着你可以自由使用、修改和分发该软件，遵循 MIT 许可证的条款和条件。有关更多详细信息，请参阅项目存储库中的 LICENSE 文件。
npm package: @iflow-mcp/cappt-team-mcp-server-nodejs
Description: A MCP server for Cappt
Author: ye4241
Latest version: 0.1.5

# mcp-server-cappt: 咔片MCP服务器

<div align="center">
  <img alt="Cappt" height="128" width="128" src="https://raw.githubusercontent.com/cappt-team/mcp-server-nodejs/main/assets/logo.png"/>
</div>

## 概述

一个MCP（Model Context Protocol）服务器，用于生成咔片（cappt.cc）的提纲和演示文稿。

### 工具

1. `generate_presentation`

- 从提纲生成演示文稿（由 `generate_outline` 生成）
- 输入：
  - `outline`（字符串）：标准提纲
  - `include_gallery`（布尔值）：是否在结果中包含图库
  - `include_preview`（布尔值）：是否在结果中包含预览
- 返回：
  - `record_id`：生成的演示文稿的记录 ID
  - `status`：生成过程的状态
  - `total_page`：演示文稿的总页数
  - `editor_url`：编辑演示文稿的 URL
  - `title`：演示文稿的标题
  - `thumbnail`：演示文稿的缩略图
  - `gallery`：演示文稿的图库
  - `preview`：演示文稿的预览

### 提示词

1. `generate_outline`

- 生成标准提纲
- 输入：
  - `input`（字符串）：用户输入，例如标题、文章等
- 返回：标准提纲

## 安装

```
cd path/to/repo
npm install
npm run build
```

## 配置

### 与 Claude Desktop 一起使用

将以下内容添加到你的 `claude_desktop_config.json` 中：

```json
{
  "mcpServers": {
    "sentry": {
      "command": "node",
      "args": [
        "path/to/repo/dist/index.js"
      ],
      "env": {
        "CAPPT_TOKEN": "YOUR_CAPPT_TOKEN"
      }
    }
  }
}
```

## 调试

你可以使用 MCP 检查器来调试服务器。请确保你已使用 `uv` 安装此软件包，然后运行：

```
npx @modelcontextprotocol/inspector node dist/index.ts
```

## 许可证

此 MCP 服务器根据 MIT 许可证授权。这意味着你可以自由使用、修改和分发该软件，遵循 MIT 许可证的条款和条件。有关更多详细信息，请参阅项目存储库中的 LICENSE 文件。
npm package: @iflow-mcp/cargo-doc-mcp
Description: A MCP for cargo doc
Latest version: 0.1.0

# cargo doc MCP Server

A MCP server for managing Rust documentation through cargo doc commands. This server provides tools to check, build, and search Rust documentation locally.

<a href="https://glama.ai/mcp/servers/l4augy7aft">
  <img width="380" height="200" src="https://glama.ai/mcp/servers/l4augy7aft/badge" alt="Cargo Doc Server MCP server" />
</a>

## Features

### Tools

- `get_crate_doc` - Get crate's main documentation page for understanding overall concepts and usage

  - Parameters:
    - `project_path`: Path to the Rust project (must be absolute path)
    - `crate_name`: Name of the crate to get documentation for

- `list_symbols` - List all symbols (structs, enums, traits, etc.) in a crate's documentation

  - Parameters:
    - `project_path`: Path to the Rust project (must be absolute path)
    - `crate_name`: Name of the crate to list symbols for

- `search_doc` - Search within a crate's documentation
  - Parameters:
    - `project_path`: Path to the Rust project (must be absolute path)
    - `crate_name`: Name of the crate to search in
    - `query`: Search query (keyword or symbol)
    - `limit` (optional): Maximum number of results to return (default: 10)

## Requirements

- Node.js 16 or later
- Rust and Cargo installed

## Installation

Install dependencies:

```bash
pnpm install
```

Build the server:

```bash
pnpm run build
```

For development with auto-rebuild:

```bash
pnpm run watch
```

## Usage

Add the following configuration:

```json
{
  "mcpServers": {
    "docs-rs-mcp": {
      "command": "/absolute/path/to/docs-rs-mcp/build/index.js"
    }
  }
}
```

## Debugging

Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector:

```bash
pnpm run inspector
```

The Inspector will provide a URL to access debugging tools in your browser.

## Cache System

The server maintains a cache of built documentation paths to improve performance. Cache entries expire after 24 hours to ensure documentation stays up-to-date.

## License

MIT
npm package: @iflow-mcp/cartographai-mcp-server-codegraph
Description: MCP server for graph representation of a codebase
Latest version: 0.2.1

# mcp-server-codegraph

A [Model Context Protocol](https://github.com/modelcontextprotocol) server that provides tools to generate and query a graph representation in your codebase.


## Features
- 📊 Creates a graph representation of your codebase
- 🔍 Identifies entities (functions, classes, imports) and their relationships
- 🔗 Tracks relationships like function calls, inheritance, and implementations
- 🌐 Supports multiple programming languages (Python, JavaScript, Rust)

## Tools

- **index**
    - Indexes the codebase to create a graph of entities and relationships.
- **list_file_entities**
    - Provides a list of all entities within a specified file.
        - `path` (string): relative path of the file
- **list_entity_relationships**
    - List the relationships of a specific entity
    - Inputs
        - `path` (string): relative path of the file
        - `name` (string): name of entity

## Usage

```sh
npx @cartographai/mcp-server-codegraph /path/to/directory
```

### Claude Desktop

Add this to your `claude_desktop_config.json`

```json
{
  "mcpServers": {
    "codegraph": {
      "command": "npx",
      "args": [
        "-y",
        "@cartographai/mcp-server-codegraph",
        "/path/to/directory",
      ]
    }
  }
}
```
npm package: @iflow-mcp/catalystneuro-mcp_read_images
Description: MCP server for analyzing images using OpenRouter vision models
Author: CatalystNeuro
Latest version: 0.1.0

# MCP Read Images

An MCP server for analyzing images using OpenRouter vision models. This server provides a simple interface to analyze images using various vision models like Claude-3.5-sonnet and Claude-3-opus through the OpenRouter API.

## Installation

```bash
npm install @catalystneuro/mcp_read_images
```

## Configuration

The server requires an OpenRouter API key. You can get one from [OpenRouter](https://openrouter.ai/keys).

Add the server to your MCP settings file (usually located at `~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json` for VSCode):

```json
{
  "mcpServers": {
    "read_images": {
      "command": "read_images",
      "env": {
        "OPENROUTER_API_KEY": "your-api-key-here",
        "OPENROUTER_MODEL": "anthropic/claude-3.5-sonnet"  // optional, defaults to claude-3.5-sonnet
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}
```

## Usage

The server provides a single tool `analyze_image` that can be used to analyze images:

```typescript
// Basic usage with default model
use_mcp_tool({
  server_name: "read_images",
  tool_name: "analyze_image",
  arguments: {
    image_path: "/path/to/image.jpg",
    question: "What do you see in this image?"  // optional
  }
});

// Using a specific model for this call
use_mcp_tool({
  server_name: "read_images",
  tool_name: "analyze_image",
  arguments: {
    image_path: "/path/to/image.jpg",
    question: "What do you see in this image?",
    model: "anthropic/claude-3-opus-20240229"  // overrides default and settings
  }
});
```

### Model Selection

The model is selected in the following order of precedence:
1. Model specified in the tool call (`model` argument)
2. Model specified in MCP settings (`OPENROUTER_MODEL` environment variable)
3. Default model (anthropic/claude-3.5-sonnet)

### Supported Models

The following OpenRouter models have been tested:
- anthropic/claude-3.5-sonnet
- anthropic/claude-3-opus-20240229

## Features

- Automatic image resizing and optimization
- Configurable model selection
- Support for custom questions about images
- Detailed error messages
- Automatic JPEG conversion and quality optimization

## Error Handling

The server handles various error cases:
- Invalid image paths
- Missing API keys
- Network errors
- Invalid model selections
- Image processing errors

Each error will return a descriptive message to help diagnose the issue.

## Development

To build from source:

```bash
git clone https://github.com/catalystneuro/mcp_read_images.git
cd mcp_read_images
npm install
npm run build
```

## License

MIT License. See [LICENSE](LICENSE) for details.