open-index/open-npm huggingface dataset:
npm package: @iflow-mcp/dcatfly-weixin-claude-code
Description: WeChat Channel for Claude Code via iLink Bot API
Latest version: 0.5.0

# weixin-claude-code

微信 Channel 插件 for Claude Code —— 通过微信与 Claude Code 双向通信。

基于 [`weixin-bot-plugin`](https://github.com/Dcatfly/weixin_bot_plugin) 微信通信库，适配 Claude Code 的 [Channel](https://docs.anthropic.com/en/docs/claude-code/channels) 功能实现。

[English](./README_EN.md)

## 功能

- 在微信中给 Claude Code 发消息，Claude 直接在终端中处理并回复到微信
- 支持文本、图片、语音、视频、文件等全媒体类型
- 微信扫码登录，零配置即用
- Claude 回复自动转为纯文本（微信不支持 Markdown）
- [权限转发](https://code.claude.com/docs/en/channels-reference#relay-permission-prompts)：Claude Code 的工具调用审批（如 Bash、Write、Edit）会转发到微信，直接回复 yes/no 即可远程授权，无需守在终端前

## 前置要求

- [Claude Code](https://docs.anthropic.com/en/docs/claude-code) v2.1.80+
- [Bun](https://bun.sh/) 运行时
- claude.ai 账号登录（不支持 API Key 认证）

## 安装

```bash
# 1. 添加插件源
/plugin marketplace add Dcatfly/claude-plugins

# 2. 安装插件
/plugin install weixin-claude-code@dcatfly-plugins
```

## 使用

### 启动

```bash
claude --dangerously-load-development-channels plugin:weixin-claude-code@dcatfly-plugins
```

> 自定义 Channel 目前处于研究预览阶段，需要使用 `--dangerously-load-development-channels` 标志启动。

### 首次登录

启动后 Claude 会提示你调用 `login` 工具，扫描二维码完成微信连接：

1. Claude 调用 login 工具，展示二维码（如被折叠按 `ctrl+o` 展开）
2. 用微信扫描二维码
3. 在微信中确认登录
4. 连接成功，开始收发消息

### 收发消息

连接成功后，你在微信中发送的消息会实时推送到 Claude Code 会话中。Claude 处理后通过 `reply` 工具将回复发回微信。

**发送文本**：直接在微信中输入文字

**发送媒体**：支持发送图片、语音、视频、文件，Claude 会下载并处理

**接收回复**：Claude 的回复会自动转为纯文本发送到微信

### 可用工具

| 工具 | 说明 |
|------|------|
| `login` | 发起微信扫码登录 |
| `reply` | 回复微信消息（支持文本和媒体） |
| `status` | 查询当前连接状态 |
| `logout` | 登出微信并清除凭证 |

### 登出

在 Claude Code 中调用 `logout` 工具即可断开微信连接并清除本地凭证。

## 工作原理

```
微信用户 <-> 微信服务器 <-> iLink Bot API <-> [本插件 MCP Server] <-> Claude Code
```

插件作为 MCP Channel 服务器运行。微信通信由 [`weixin-bot-plugin`](https://github.com/Dcatfly/weixin_bot_plugin) 库处理（iLink Bot API long-poll 收消息、CDN 媒体加解密、SILK 语音转码等），本插件将收到的消息以 Channel notification 推送到 Claude Code 会话中。Claude 通过 reply 工具将回复发回微信。

## 局限性

- **仅接受登录者自己的消息** —— 其他人发的消息和群消息会被过滤，这是安全设计
- **不支持 Claude Code 原生命令** —— 微信消息是作为对话内容处理的，无法触发 `/clear`、`/compact` 等 CLI 命令
- **Session 过期需手动重新登录** —— 微信 session 过期后需要重新调用 `login` 扫码
- **Channel 功能处于研究预览阶段** —— 需要 `--dangerously-load-development-channels` 标志
- **需要 claude.ai 登录** —— 不支持 Console 或 API Key 认证
- **非 Channel 模式下插件仍然会启动** —— MCP 协议目前无法让插件检测自身是否运行在 channel 模式（[claude-code#36964](https://github.com/anthropics/claude-code/issues/36964)）。若以普通 MCP 模式加载，插件仍会消费微信消息但 notification 会被静默丢弃，导致后续 channel 模式会话丢失这些消息。建议仅在需要时启用此插件

## 数据存储

凭证和同步数据存储在 `~/.claude/channels/wechat/` 目录下：

```
~/.claude/channels/wechat/
├── accounts.json              # 账号列表
├── accounts/<id>.json         # 登录凭证（仅所有者可读）
└── sync/<id>.sync.json        # 消息同步断点
```

登出时所有数据会被自动清除。

收发的媒体文件（图片、语音、视频、文件）缓存在系统临时目录下：

```
$TMPDIR/weixin-claude-code/media/
├── inbound/     # 接收的媒体
└── outbound/    # 发送的媒体
```

## 许可证

MIT
npm package: @iflow-mcp/dcspark-mcp-server-defillama
Description: MCP server for DefiLlama API
Latest version: 1.0.2

# MCP Server for DefiLlama

This repository contains a Model Context Protocol (MCP) server that provides Claude with access to DeFi data via the DefiLlama API. The server enables Claude to perform operations like retrieving protocol TVL data, chain TVL data, token prices, and stablecoin information.

## Overview

The MCP server exposes the following tools to Claude:

### Protocol Data

- `defillama_get_protocols`: List all protocols tracked by DefiLlama
- `defillama_get_protocol_tvl`: Get TVL data for a specific protocol

### Chain Data

- `defillama_get_chain_tvl`: Get TVL data for a specific chain

### Token Data

- `defillama_get_token_prices`: Get current prices of tokens
- `defillama_get_historical_prices`: Get historical prices of tokens

### Stablecoin Data

- `defillama_get_stablecoins`: List all stablecoins tracked by DefiLlama
- `defillama_get_stablecoin_data`: Get data for a specific stablecoin

Prerequisites
- Node.js (v16 or higher)

## Running the server

### Option 1: Using npx (Recommended)
```npx @mcp-dockmaster/mcp-server-defillama```
You can run the MCP server directly without installation using npx:
This will download and execute the server directly from npm.

### Option 2: Manual Installation
Clone this repository:
```git clone https://github.com/mcp-dockmaster/mcp-server-defillama.git```
Install dependencies:
```npm install```
Build the project:
```npm run build```

## Claude Usage
To configure Claude Desktop to use this MCP server:

Open Claude Desktop, and navigate to Settings>Developer Settings>MCP Servers. There you'll encounter the button to open the configuration JSON file.

Add the MCP server configuration:
```json
{
  "mcpServers": {
    "mcp-server-defillama": {
      "command": "npx",
      "args": [
        "@mcp-dockmaster/mcp-server-defillama"
      ]
    }
  }
}
```
Alternatively, if you installed the package locally:
```json
{
  "mcpServers": {
    "mcp-server-defillama": {
      "command": "node",
      "args": [
        "/path/to/your/mcp-server-defillama/build/index.js"
      ]
    }
  }
}
```
npm package: @iflow-mcp/deepseek-thinker-mcp
Description: A MCP server for get deepseek COT
Latest version: 1.0.2

# Deepseek Thinker MCP Server

[![smithery badge](https://smithery.ai/badge/@ruixingshi/deepseek-thinker-mcp)](https://smithery.ai/server/@ruixingshi/deepseek-thinker-mcp)

 A MCP (Model Context Protocol) provider Deepseek reasoning content to MCP-enabled AI Clients, like Claude Desktop. Supports access to Deepseek's thought processes from the Deepseek API service or from a local Ollama server.

<a href="https://glama.ai/mcp/servers/d7spzsfuwz"><img width="380" height="200" src="https://glama.ai/mcp/servers/d7spzsfuwz/badge" alt="Deepseek Thinker Server MCP server" /></a>

## Core Features

- 🤖 **Dual Mode Support**
  - OpenAI API mode support
  - Ollama local mode support

- 🎯 **Focused Reasoning**
  - Captures Deepseek's thinking process
  - Provides reasoning output

## Available Tools

### get-deepseek-thinker
- **Description**: Perform reasoning using the Deepseek model
- **Input Parameters**:
  - `originPrompt` (string): User's original prompt
- **Returns**: Structured text response containing the reasoning process

## Environment Configuration

### OpenAI API Mode
Set the following environment variables:
```bash
API_KEY=<Your OpenAI API Key>
BASE_URL=<API Base URL>
```

### Ollama Mode
Set the following environment variable:
```bash
USE_OLLAMA=true
```

## Usage

### Integration with AI Client, like Claude Desktop
Add the following configuration to your `claude_desktop_config.json`:

```json
{
  "mcpServers": {
    "deepseek-thinker": {
      "command": "npx",
      "args": [
        "-y",
        "deepseek-thinker-mcp"
      ],
      "env": {
        "API_KEY": "<Your API Key>",
        "BASE_URL": "<Your Base URL>"
      }
    }
  }
}
```

### Using Ollama Mode
```json
{
  "mcpServers": {
    "deepseek-thinker": {
      "command": "npx",
      "args": [
        "-y",
        "deepseek-thinker-mcp"
      ],
      "env": {
        "USE_OLLAMA": "true"
      }
    }
  }
}
```
### Local Server Configuration

```json
{
  "mcpServers": {
    "deepseek-thinker": {
      "command": "node",
      "args": [
        "/your-path/deepseek-thinker-mcp/build/index.js"
      ],
      "env": {
        "API_KEY": "<Your API Key>",
        "BASE_URL": "<Your Base URL>"
      }
    }
  }
}
```

## Development Setup

```bash
# Install dependencies
npm install

# Build project
npm run build

# Run service
node build/index.js
```

## FAQ

### Response like this: "MCP error -32001: Request timed out"
This error occurs when the Deepseek API response is too slow or when the reasoning content output is too long, causing the MCP server to timeout.

## Tech Stack

- TypeScript
- @modelcontextprotocol/sdk
- OpenAI API
- Ollama
- Zod (parameter validation)

## License

This project is licensed under the MIT License. See the LICENSE file for details.