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Open-source MCP server for AI agents: web search, content extraction, and library docs.
io.github.n24q02m/wet 是 ChatHome Skills 收录的 MCP 条目，来源为 Official MCP Registry，质量分 78/100。 作为 Model Context Protocol 服务器，它向 Claude、Cursor、Copilot 等兼容 MCP 的 LLM 客户端暴露可直接调用的工具，让 agent 的能力超出模型内置范围。 该条目由 ChatHome Skills 的每日抓取从官方 registry 和 GitHub 发现查询中收录，每次运行都会重新核验仓库事实、新鲜度信号和安装元数据。 质量分综合了官方 registry 收录情况、可用安装命令、许可证完整性、仓库活跃度和 GitHub star 趋势，完整算法说明见来源页。安装命令已列于上方，可直接复制使用。 在将 io.github.n24q02m/wet 加入生产 agent 工作流前，请核对上方的最近更新日期和仓库状态——下方事实快照是最近一次抓取的时点值。
uvx wet-mcp==3.19.2运行此命令前，请先核对上游源码。
{
"_meta": {
"io.modelcontextprotocol.registry/official": {
"status": "active",
"isLatest": true,
"updatedAt": "2026-10-05T02:27:21.441508Z",
"publishedAt": "2026-10-05T02:27:21.441508Z",
"statusChangedAt": "2026-10-05T02:27:21.441508Z"
}
},
"server": {
"name": "io.github.n24q02m/wet",
"$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
"version": "3.19.2",
"packages": [
{
"version": "3.19.2",
"transport": {
"type": "stdio"
},
"identifier": "wet-mcp",
"runtimeHint": "uvx",
"registryType": "pypi",
"environmentVariables": [
{
"name": "API_KEYS",
"isSecret": true,
"description": "Provider API keys (format: PROVIDER_API_KEY:key,...). Select models per task with EMBEDDING_MODELS / RERANK_MODELS / LLM_MODELS (CSV provider/model, order = litellm fallback); provider is inferred from the model prefix. Empty embedding/rerank chain falls back to fastretrieval's built-in local reference model."
},
{
"name": "GITHUB_TOKEN",
"isSecret": true,
"description": "GitHub personal access token for higher rate limits on library discovery"
}
]
}
],
"repository": {
"url": "https://github.com/n24q02m/wet.git",
"source": "github"
},
"description": "Open-source MCP server for AI agents: web search, content extraction, and library docs."
}
}