通用 AI 搜索 MCP 服务器，支持任何兼容 OpenAI API 格式的 AI 模型
通用 AI 搜索 MCP 服务器，支持任何兼容 OpenAI API 格式的 AI 模型. Exposed over MCP by the ai mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
编辑配置文件（Kiro: .kiro/settings/mcp.json | Claude Desktop: claude_desktop_config.json）：
The server ships on PyPI as ai-search-mcp, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
Everything the assistant can do here goes through one of these:
api_url — ✅api_key — ✅search_model_id — ✅analysis_model_id — ❌timeout — ❌stream — ❌filter_thinking — ❌analysis_retry_count — ❌search_retry_count — ❌max_query_plan — ❌This sits in the AI and media services group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Ai's toolset — api_url, api_key, search_model_id and 7 more — is a fair guide to whether it matches your workflow. It is maintained by lianwusuoai; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| api_url | ✅ |
| api_key | ✅ |
| search_model_id | ✅ |
| analysis_model_id | ❌ |
| timeout | ❌ |
| stream | ❌ |
| filter_thinking | ❌ |
| analysis_retry_count | ❌ |
| search_retry_count | ❌ |
| max_query_plan | ❌ |
**配置**：
```json
{
"mcpServers": {
"ai-search": {
"url": "http://localhost:11000/sse?key=xinchen"
}
}
}Configuration as documented by the project. Restart the client after saving.
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.