Ragmetric MCP runs on your own machine — the client starts it, so there is no endpoint to ping. 49 installs a week from npm. Last commit 13 Jun 2026.
RAG retrieval IR metrics: recall@k, hit@k, MRR, NDCG@k, evaluate_batch.
We read the source, 21 h ago · rules 3dff92dd89df
What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.
этот файл ставится пользователю, но в репозитории его нет
Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.
We found places where it runs commands, builds paths or queries from values it is given. None of that is a flaw by itself — it becomes one when the code changes, and code changes quietly between releases. We re-read it on every one.
This server runs on your own machine — install it with the package manager and the client starts it for you. Package name taken from the official registry entry.
claude mcp add ragmetric-mcp -- npx -y @mukundakatta/ragmetric-mcp
{
"mcpServers": {
"ragmetric-mcp": {
"args": [
"-y",
"@mukundakatta/ragmetric-mcp"
],
"command": "npx"
}
}
}
[mcp_servers.ragmetric-mcp]
command = "npx"
args = ["-y", "@mukundakatta/ragmetric-mcp"]
{
"mcpServers": {
"ragmetric-mcp": {
"args": [
"-y",
"@mukundakatta/ragmetric-mcp"
],
"command": "npx"
}
}
}
{
"mcpServers": {
"ragmetric-mcp": {
"args": [
"-y",
"@mukundakatta/ragmetric-mcp"
],
"command": "npx"
}
}
}
Minimal RAG-over-a-corpus MCP retrieval: search_knowledge returns cited chunks. Local embeddings.
Deterministic multi-hop graph retrieval for RAG. Zero LLM calls in the query path.
Local RAG MCP for markdown documentation. Retrieval only; host synthesizes answers.
Hybrid vector + reasoning retrieval, agent memory, multi-agent orchestration, MCP server, and RAG.
Local-first agentic RAG with citations - hybrid search, reranking, multimodal document retrieval
Private on-device memory & retrieval for AI assistants — offline vector + lexical search.
Graph + vector memory for agents: recall, ingest, search, distill. Local or remote backend.
Trace, evaluate, and optimize your LLM, RAG, and agent apps with Noveum observability.
Answers built from our own checks of this server.