Rag MCP

Name: Rag MCP
Author: jaimenbell

by jaimenbellversion 0.1.0i★ –iRuns on your computer

Registry nameio.github.jaimenbell/rag-mcp

Install

Overview

Description from the maker (original English)

Minimal RAG-over-a-corpus MCP retrieval: search_knowledge returns cited chunks. Local embeddings.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "rag": {
      "command": "uvx",
      "args": [
        "jaimenbell-rag-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add rag -- uvx jaimenbell-rag-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "rag": {
      "command": "uvx",
      "args": [
        "jaimenbell-rag-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "rag": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "jaimenbell-rag-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account jaimenbell, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 5654 servers in this category
Published to the registry more recently than 25% of the other 5654 servers in this category
Basic checks equal to or better than 100% of the other 5654 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Rag MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.0) reached the registry 4 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated yesterday

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 21 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 2 months ago

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

rag-mcp

A minimal, honest RAG-over-a-corpus MCP retrieval tool. One tool,

search_knowledge(query, k, doc_class=None), that embeds a query, vector-searches a local

corpus, and returns passages with citations (source + heading + chunk index) so answers

are traceable.

Built to slot into the mcp-factory manifest model. Fully local + $0 (no paid embedding API).

Why it's safe to put in front of a real corpus

Cited - every hit carries source + heading + chunk_index.
Auth-scoped - results are confined to the configured corpus root; sources that escape it (absolute paths, .. traversal) are refused.
Fail-soft - a down or empty store returns a *structured error*, never an exception that crashes the calling agent.
Bounded - k is clamped to [1, 20]; empty queries are rejected.
Version-pinned deps (requirements.txt).

Stack

Layer	Choice
Embeddings	local ONNX `all-MiniLM-L6-v2` (384-dim, CPU, $0) -- default. `bge-large-en-v1.5` (1024-dim, 512-token context) available opt-in via `RAG_MCP_EMBEDDER=bge`; see CUTOVER.md.
Vector store	ChromaDB embedded `PersistentClient` (zero-infra)
Server	`mcp` Python SDK 2.x, stdio transport, protocol revision 2026-07-28

Protocol revision

Pinned to mcp==2.0.0, the first SDK release implementing MCP protocol revision 2026-07-28. The server serves both eras on the same stdio connection -- the client's first frame picks:

Client opens with	Negotiated revision	Notes
a per-request `_meta` envelope (or a `server/discover` probe)	`2026-07-28`	stateless per-request envelope; no `initialize`
the classic `initialize` handshake	`2025-11-25`	handshake era caps here -- expected, not a downgrade

2026-07-28 is not reachable via the `initialize` handshake; it is a "modern" revision reached through server/discover or an inline _meta version stamp. Era selection is automatic and per-connection -- there is no server-side flag.

tests/test_protocol_version.py asserts both paths end-to-end, so a dependency rollback that silently drops the server to an older revision fails CI instead of passing quietly.

Quick start

python -m venv .venv && .venv/Scripts/python -m pip install -r requirements.txt

Ingest a corpus (markdown). Incremental by default: only files whose content

changed since the last run are re-embedded.

python -m rag_mcp.cli ingest path/to/docs --db ./store.chroma

Force a rebuild in place (ignore the manifest, re-embed everything)

python -m rag_mcp.cli ingest path/to/docs --db ./store.chroma --full

One-off query (corpus root = the auth scope)

python -m rag_mcp.cli query "your question" --db ./store.chroma --corpus path/to/docs -k 5

Same, restricted to one doc_class ("note" or "handoff" -- see "Filtering by

document class" below)

python -m rag_mcp.cli query "your question" --db ./store.chroma --corpus path/to/docs --doc-class note

Run as an MCP server (stdio); configure via env first

RAG_MCP_CORPUS_ROOT, RAG_MCP_DB_PATH, RAG_MCP_COLLECTION, RAG_MCP_EMBEDDER

python run_server.py # operational entrypoint (referenced by mcp.yaml) python -m rag_mcp # same server, via the packaged console entry point rag-mcp # after pip install jaimenbell-rag-mcp -- console script

Keeping the index fresh (incremental ingest)

Ingest is incremental by default. A manifest inside the store dir records a SHA-256 of each file's decoded text; a run re-embeds only what actually changed, and prunes what upsert alone never could (chunks of deleted/renamed notes, and trailing chunks of notes that got shorter).

Measured on a live 2808-file / 26.6 MiB corpus (bge, CPU):

Run	Cost
tick with no changes	~0.7s (walk + read + hash everything)
full re-embed	~2h33m (50,109 chunks at ~5.5 chunks/sec)

That is what makes a frequent schedule affordable: reingest.bat is meant to run every 15 minutes instead of once daily at 03:00, which had left a note written at 03:05 invisible to search_knowledge for nearly 24 hours.

The manifest is only trusted when the run identity matches -- embedder, embedding dimension, collection and chunking parameters. Change any of them and every file is re-embedded, so an embedder swap can never be silently half-applied. A missing, corrupt, or mismatched manifest, or a manifest against an empty store, all degrade to a full rebuild; nothing degrades to a wrong skip.

Snapshot de-duplication

The manifest's skip is a whole-file hash, so it cannot see the duplication that actually hurts retrieval: a daily snapshot series (fleet-health-2026-07-23.md and friends) repeats yesterday's paragraphs verbatim inside a file whose hash still changed. Measured on the live vault, one ## RED Bots status line took five distinct values across fifteen consecutive files and crowded a top-10 with byte-identical copies of itself, burying the document that explained it at rank 16.

Ingest therefore also de-duplicates at chunk level, but only within a dated series and only against the *immediately preceding* snapshot. The first occurrence is always embedded and keeps its own date as its source; later verbatim repeats are not embedded, and instead extend the survivor's repeat_dates metadata, which search_knowledge returns as snapshot_date / also_unchanged_on / snapshots_covered. So "what did this say on date X" is still answerable -- that is why the series is de-duplicated rather than excluded. A value that changes and later returns is kept, because it is a new fact rather than a repeat.

Scope is narrow and stated with the rule in rag_mcp/snapshots.py: filename ending in -YYYY-MM-DD, at least 3 such files sharing a directory and stem, byte-identical under an identical heading. On the live corpus that is 316 of 2,814 files and collapses 842 of 50,428 chunks (17.5% of series chunks, 1.67% corpus-wide) while touching zero ordinary notes. Disable with --no-snapshot-dedupe.

--full rebuilds in place (ignores the manifest, keeps the store); --clean deletes the store first. Both still WRITE a manifest, so the next run is cheap. reingest-clean.bat (weekly) remains a belt-and-braces reset.

As an MCP server

Register via mcp.yaml (validated against mcp-factory's Manifest loader). The tool is search_knowledge(query, k, doc_class=None); it reads the store configured by the RAG_MCP_* env vars.

Filtering by document class

Every chunk's metadata carries a doc_class, set at ingest time. It is "handoff" when the doc's YAML frontmatter has type: handoff or a tags entry of handoff (case-insensitive), or -- since a doc's frontmatter is optional and the session mirrors this exists to flag often carry none -- when the file sits directly under a handoff_mirror_dir (default context/, configurable via ingest()'s handoff_mirror_dir=/handoff_mirror_basenames= params or the CLI's --handoff-mirror-dir/--handoff-mirror-basename flags) and is named handoff.md / active.md / resume.md, or matches an anchored "handoff" filename token (e.g. handoff-2026-09-03.md, morning-dispatch-handoff.md) -- never a bare substring, so a title that merely mentions the word (handoff-skill-redesign-spec.md) stays "note". Everything else defaults to "note". Pass doc_class to scope a query to one class, e.g. to keep an agent's own session/handoff bookkeeping out of a knowledge lookup:

from rag_mcp.search import search_knowledge

search_knowledge( "what did we decide about X", k=5, store=store, corpus_root=root, doc_class="note", )

doc_class is a validated, case-sensitive enum -- "note" or "handoff" (see rag_mcp.search.ALLOWED_DOC_CLASSES) -- or omitted for no filter. A value outside that set (wrong case, a typo, any other type) returns a structured invalid_doc_class error, same shape as invalid_query. A syntactically valid doc_class that simply has no matches in the current store still fails soft to an empty, ok: true result.

An incremental ingest run backfills doc_class (and any other metadata-schema change) onto already-embedded, content-unchanged chunks WITHOUT re-embedding them -- see ingest.CURRENT_METADATA_VERSION. A store that predates this feature entirely gets the correct doc_class on every chunk after exactly one incremental run, not a full --clean rebuild.

Tests

python -m pytest        # 272 passed

This count is the total collected, CI-enforced against the live pytest summary (scripts/check_readme_counts.py, wired into .github/workflows/ci.yml). One test is platform-conditional -- WINDOWS_ONLY in tests/test_reindex_handle_release.py only runs on Windows -- so Linux CI reports 271 passed, 1 skipped for the same 272-test suite; the gate compares passed-plus-skipped against this claim, not passed alone, so that expected skip does not read as drift.

Layout

rag_mcp/

chunking.py heading-scoped, overlapping markdown chunks store.py VectorStore (Chroma) + Embedder protocol (MiniLM default + BgeEmbedder opt-in + offline HashEmbedder) ingest.py idempotent ingest pipeline with source/heading/chunk-index metadata; incremental by default manifest.py per-file content hashes -> skip unchanged files, prune stale chunks search.py search_knowledge: cited, auth-scoped, fail-soft, bounded server.py MCP stdio server exposing search_knowledge config.py env-driven Config cli.py ingest + query CLI main.py console entrypoint (python -m rag_mcp / rag-mcp script); fails loud on missing config run_server.py operational MCP entrypoint (referenced by mcp.yaml) mcp.yaml manifest (mcp-factory model)

Commercial support

Maintained by Jaimen Bell. For production MCP integrations, custom servers, or agent-reliability work, see jaimenbell.dev.

Building your own MCP server? The MCP Starter Kit has templates, a build playbook, and packaging war-stories from shipping this one.

mcp-name: io.github.jaimenbell/rag-mcp

Rag MCP

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

rag-mcp

Why it's safe to put in front of a real corpus

Stack

Protocol revision

Quick start

Ingest a corpus (markdown). Incremental by default: only files whose content

changed since the last run are re-embedded.

Force a rebuild in place (ignore the manifest, re-embed everything)

One-off query (corpus root = the auth scope)

Same, restricted to one doc_class ("note" or "handoff" -- see "Filtering by

document class" below)

Run as an MCP server (stdio); configure via env first

RAG_MCP_CORPUS_ROOT, RAG_MCP_DB_PATH, RAG_MCP_COLLECTION, RAG_MCP_EMBEDDER

Keeping the index fresh (incremental ingest)

Snapshot de-duplication

As an MCP server

Filtering by document class

Tests

Layout

Commercial support