RAGMap is a lightweight MCP Registry-compatible subregistry + MCP server focused on **RAG-related MCP servers**.
Most AI and media services work still happens through a UI a human drives. Ragmap MCP server moves it into the conversation instead. RAGMap is a lightweight MCP Registry-compatible subregistry + MCP server focused on RAG-related MCP servers.
Because this one is hosted, setup is mostly authentication — you point your client at the endpoint and approve access. Nothing runs on your machine, so there is no runtime to keep patched.
Configuration is passed through the environment: OPENAI_API_KEY, INGEST_TOKEN, API_BASE_URL. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
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. It is maintained by khalidsaidi; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Ragmap's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
{
"mcpServers": {
"ragmap": {
"command": "npx",
"args": ["-y", "@khalidsaidi/ragmap-mcp"],
"env": {
"OPENAI_API_KEY": "your-value",
"INGEST_TOKEN": "your-value",
"API_BASE_URL": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| INGEST_TOKEN | Credential the server authenticates with. | Yes |
| API_BASE_URL | Endpoint or connection string the server talks to. | Yes |
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.