接入你的智能体
发布者自己的命令。不是 PlugMCP 端点。
用我们的密钥与日志托管一份副本。
uvx jaimenbell-rag-mcp==0.1.0Claude Code 没有针对本地命令的安装链接。复制下方命令并在终端中运行。
claude mcp add io.github.jaimenbell-rag-mcp -- uvx jaimenbell-rag-mcp==0.1.0在终端中运行。
{ "mcpServers": { "io.github.jaimenbell-rag-mcp": { "command": "uvx", "args": [ "jaimenbell-rag-mcp==0.1.0" ] } }}claude_desktop_config.json
远程 URL 还是本机命令？ 两种安装方式的区别
来自发布者
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.
Minimal RAG-over-a-corpus MCP retrieval: search_knowledge returns cited chunks. Local embeddings.
Built to slot into the mcp-factory manifest model. Fully local + $0 (no paid embedding API).
source + heading + chunk_index... traversal) are refused.k is clamped to [1, 20]; empty queries are rejected.requirements.txt).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:
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.
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
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):
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.
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.
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.
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.
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.
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)
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
Minimal RAG-over-a-corpus MCP retrieval: search_knowledge returns cited chunks. Local embeddings.
{ "mcpServers": { "io.github.jaimenbell-rag-mcp": { "command": "uvx", "args": [ "jaimenbell-rag-mcp==0.1.0" ] } }}工具
暂无工具列表。远程服务器在首次健康检查后会获得工具列表。
常见问题
在本页复制适用于 Cursor、Claude Desktop、Claude Code 或 VS Code 的 stdio 配置。它使用发布者的安装方式，而不是 PlugMCP 代理。
此记录未列出必需的环境变量。
Rag 通过 stdio 在本地运行。
上次检查时间：2026-11-09。 Minimal RAG-over-a-corpus MCP retrieval: search_knowledge returns cited chunks. Local embeddings.