Lite-MCP-Client是一个基于命令行的轻量级MCP客户端工具
Lite MCP Client MCP server is a locally run integration for AI assistants that speak the Model Context Protocol. Lite-MCP-Client是一个基于命令行的轻量级MCP客户端工具.
Lite-MCP-Client是一个基于命令行的轻量级MCP客户端工具
Once Lite MCP Client is connected, these are the calls the assistant has available:
connect-all — 连接到所有默认服务器clear — history (clh) - 清除 'ask' 命令的对话历史记录help — 显示帮助信息asyncio — 异步IO支持mcp — MCP协议客户端库langchain_openai — OpenAI模型集成langchain_google_genai — Google生成式AI模型集成langchain_anthropic — Anthropic Claude模型集成langchain_aws — AWS Bedrock模型集成dotenv — 环境变量管理type — 字符串args — 列表lite-mcp-client on PyPI is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Lite MCP Client's toolset — connect-all, clear, help and 11 more — is a fair guide to whether it matches your workflow. It is maintained by sligter; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| connect-all | 连接到所有默认服务器 |
| clear | history (clh) - 清除 'ask' 命令的对话历史记录 |
| help | 显示帮助信息 |
| asyncio | 异步IO支持 |
| mcp | MCP协议客户端库 |
| langchain_openai | OpenAI模型集成 |
| langchain_google_genai | Google生成式AI模型集成 |
| langchain_anthropic | Anthropic Claude模型集成 |
| langchain_aws | AWS Bedrock模型集成 |
| dotenv | 环境变量管理 |
| type | 字符串 |
| args | 列表 |
| env | 对象 |
| url | 字符串 |
{
"mcpServers": {
"lite-mcp-client": {
"command": "uvx",
"args": ["lite-mcp-client"]
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.