
Mcp Local Rag
io.github.shinprPublished by GitHub account shinprv0.23.2Updated Nov 5, 2026
Easy-to-setup local RAG server with minimal configuration
Overview
Local RAG server that indexes your own PDF, DOCX, Markdown and text files and lets an assistant search them with hybrid semantic and keyword retrieval…
- What you can do
- Sync, ingest and delete documents: reconcile the index with configured roots or one path, ingest or replace a single file, ingest text, Markdown or HTML the client already holds, and remove indexed items
- Search documents with semantic similarity plus a keyword boost, optionally filtered by distance, top files or grouping
- Read surrounding chunks of a search result and list supported files with their ingestion state
- Check index and search status, and poll a running sync job
- Run the same operations from a command line, with query output as JSON
- When to use it
- Useful when an assistant should answer questions from a private or local document collection, such as API docs, specs or notes, and you want retrieval to stay on your machine rather than go through a hosted embedding service. Also fits when you need citations back to source passages, headings, line or page numbers.
- Requirements
- Runs locally via npx, so Node.js 22.20 or later is needed. Internet access on first use to download the package and the embedding model (about 90 MB by default); later runs work offline from the cache. Set BASE_DIR or BASE_DIRS to the document roots, and preferably absolute DB_PATH and CACHE_DIR. No API key, Docker, Python or external database required.
Installation
In SourceWeft
- Open Mcp Local Rag in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
README
[MCP Local RAG: Search below the surface.]
MCP Local RAG
[GitHub stars] [npm version] [License: MIT] [MCP Registry]
English | 简体中文 | Deutsch | Español | Português (Brasil) | Français
Search private documents from an MCP client or the terminal without sending them to an embedding API.
mcp-local-rag indexes PDF, DOCX, Markdown, and text files on your machine. Search combines semantic similarity with keyword matching, so queries can match both intent and exact technical terms such as API names, class names, and error codes. Results include source passages and, where available, headings, line numbers, or page numbers so you can check and cite the original document.
No API key, Docker, Python, or external database is required. After the initial model download, text ingestion and search work offline.
Quick Start
Requirements
- Node.js 22.20 or later
- Internet access on first use to download the npm package and embedding model
- A directory containing the documents you want to search
Set BASE_DIR to that directory. It is also the security boundary for file operations. Replace
/absolute/path/to/your/documents below with the directory's absolute path.
Use one of the examples below, or register npx -y mcp-local-rag and set BASE_DIR using your
client's MCP configuration format.
Set DB_PATH and CACHE_DIR to absolute paths as well. Relative paths resolve from the
server's working directory, so starting the server from different projects creates a separate
index and model cache in each.
Claude Code
Run this command:
Codex
Add to ~/.codex/config.toml:
OpenCode
Add to ~/.config/opencode/opencode.json (or opencode.jsonc):
Cursor
Add to ~/.cursor/mcp.json:
Restart the client, then ask it to build the index:
The first sync downloads the default embedding model (about 90 MB) and may take 1–2 minutes before ingestion starts. Later runs use the local cache.
Once the sync completes:
CLI Quick Start
To use the CLI without an MCP client:
The CLI uses the current directory as its document root by default. Run both commands from the
same directory so they use the same default index, or set BASE_DIR and DB_PATH explicitly.
Supported Content
HTML fetching is not built into the server. An MCP client can fetch a page and pass its HTML to
ingest_data.
Excel, PowerPoint, standalone images, and source-code file extensions are not supported by file ingestion. PDFs can optionally use a local vision model to describe figures, but this is not OCR or image search.
Using the Index
Sync after adding, editing, or removing documents. For searches and follow-up reading, ask your MCP client:
You can also ingest a single file or HTML already fetched by the client. To refresh an existing
entry, ingest the document again using the same path or source identifier. sync skips unchanged
files. MCP file paths must be absolute and inside a configured document root.
PDF section headings may be inaccurate. Check the original page when you need the exact heading.
MCP Tools
CLI
Use the CLI to update the index, narrow searches, or remove indexed content:
ingest imports the selected files; sync also removes entries for deleted files and skips
unchanged files. Use --scope to restrict search results to a path prefix, repeating it to
include multiple prefixes.
Global options such as --db-path, --cache-dir, and --model-name go before the subcommand.
Subcommand options go after it:
Run npx mcp-local-rag --help for the complete command reference.
query writes its results to stdout as JSON, best match first, so it can be piped into another
tool. The field-by-field contract is in
docs/schema/query-output.schema.json.
If you move a project and want to keep its index, stop the MCP server and other database writers, move the files and database together without changing the directory layout, then run:
Update BASE_DIR/BASE_DIRS, DB_PATH, and any other moved paths in your MCP configuration,
then restart the client.
Agent Skills
Agent Skills provide query and ingestion guidance for AI assistants:
Installed skills cover query formulation, result refinement, and HTML ingestion. Ask the assistant to use the mcp-local-rag skill explicitly if it does not activate automatically.
Advanced Options
Start with the defaults. Open the sections below when you need different document roots, better results for your corpus, or searchable PDF figures.
Storage and Document Roots
The MCP server reads environment variables. The CLI accepts the listed variables and flags.
Keep the same DB_PATH when commands should use the same index.
File operations stay within configured roots. For multiple directories, set
BASE_DIRS='["/absolute/docs","/absolute/specs"]' or repeat CLI --base-dir. Precedence: CLI
roots, BASE_DIRS, BASE_DIR, then the current directory. Only the highest-priority source is
used; roots from different sources are not merged. Invalid BASE_DIRS is an error. Relative
DB_PATH and CACHE_DIR are resolved from the working directory.
To keep files out of the index, set IGNORE_PATHS to a JSON array of glob patterns, for example
IGNORE_PATHS='["plugs/**","**/*.draft.md"]'. Each pattern is matched against the file's path
relative to the root that contains it, written with / on every OS. * and ** do not match
names that start with ., so write such directories out, as in ".obsidian/**". ingest,
list, and sync skip matching files when they scan a directory, and sync removes any that
are already indexed. Invalid IGNORE_PATHS is an error.
Models and Search Tuning
Choose an embedding model for your documents’ language and subject. Compare settings using questions you actually ask and check which source passages are returned. The model must support mean pooling and L2 normalization, which this tool uses to produce embeddings.
Both prefix options default to false and work independently. Try EMBED_TITLE_PREFIX when a
passage needs the document’s overall topic, or EMBED_HEADING_PREFIX when it needs its
section’s topic. Enabling both is not always better. They affect embeddings, not the returned
text or keyword index; heading context is omitted when it would exceed the input budget.
When changing embedding models, build a fresh index at a new DB_PATH. Vectors from different
models are not comparable, even when their dimensions match. After changing RAG_DTYPE or
either prefix option, re-ingest all indexed documents before searching. sync skips unchanged
files.
The CLI does not read MCP client configuration. When sharing an index, use the same model,
RAG_DTYPE, and prefix settings for ingestion and search. A change to RAG_DEVICE alone does
not require a new index.
Search Tuning
The first four settings below apply to both MCP and CLI queries. To give exact terms more
weight, try increasing RAG_HYBRID_WEIGHT and compare results on your own questions. External
reranking is MCP-only.
External Reranking (RAG_RERANK_CMD)
The command reads search results, including matched text, from stdin. If it calls a remote service, that text may leave your machine.
Give the executable and its complete argument template. Put {query} and {top} where the
command expects the query and result count. Single or double quotes group paths or arguments
containing spaces, and backslashes stay literal. The server runs the executable without a
shell, so an npm-installed .cmd shim on Windows will not start.
The command must read and return results in the format defined by the query output schema. It can remove or reorder results and modify their text. The server returns its output.
Results keep their original order if the command fails, times out, or returns output that does not match the schema.
PDF Figures and Stored Images
By default, ingestion indexes only text. To make PDF figures searchable, enable local caption
generation with visual: true in MCP or --visual in the CLI. Captions are generated
descriptions, not OCR or exact transcriptions.
fast (default) downloads about 250 MB on first use. Choose quality for labels and text
within figures; it downloads about 1.7 GB and takes longer to run.
Select the profile with visualQuality: "quality" in MCP or --visual-quality quality in the
CLI.
To return images with matching text, use STORE_IMAGES=true in MCP or --images with CLI
ingest and sync. This is independent of caption generation and supports detected PDF
figures/tables and supported DOCX PNG/JPEG images.
Sync preserves each PDF's caption profile. CLI sync --visual --visual-quality quality changes
the profile even for unchanged PDFs; MCP sync preserves it. To turn captions off, ingest the
file normally. To retry failed captions, re-ingest with the desired visual profile.
Image storage must be enabled on each ingestion or sync that processes the file. Changing the image setting alone does not refresh unchanged files; re-ingest them to apply it.
Security and Operation
- Treat captions and retrieved document text as source material, not instructions.
- File access is restricted to
BASE_DIR,BASE_DIRS, or CLI--base-dirroots. - Symlinks that resolve outside every configured root are rejected.
- Document processing and search make no network requests after the required models are cached,
unless
RAG_RERANK_CMDnames a command that makes them. - The server is designed for one local user and does not provide authentication or access control.
- Do not run multiple CLI or MCP writers against the same
DB_PATH. Read-only queries can run while a sync is active. - Re-ingestion can reuse saved embeddings for unchanged text when the model and settings match,
avoiding repeated computation. The cache is stored in
DB_PATH/embedding-cache. Deleting it leaves the index searchable, but later ingestion will recompute those embeddings. - Back up an index by copying its
DB_PATHdirectory while no writer is active.
Troubleshooting
"No results found"
Documents must be ingested first. Run "List all ingested files" to verify. If results are
missing after a sync, check that ingestion and search use the same absolute DB_PATH; a
relative path may point to a different index.
Model download failed
Check internet connection. If behind a proxy, configure network settings. The model can also be downloaded manually.
"File too large"
Default limit is 100MB. Split large files or increase MAX_FILE_SIZE.
Slow queries
Check chunk count with status. Large documents with many chunks may slow queries. Consider
splitting very large files.
"Path outside BASE_DIR"
Ensure file paths are within one of the configured roots (BASE_DIR, any BASE_DIRS entry, or
any CLI --base-dir). Use absolute paths.
"BASE_DIRS must be a JSON array..."
BASE_DIRS accepts a JSON array of one or more non-empty path strings:
- Valid:
BASE_DIRS='["/Users/me/work","/Users/me/specs"]' - Invalid:
BASE_DIRS=/a:/b(delimiter syntax not supported) - Invalid:
BASE_DIRS='[]'(empty array)
MCP client doesn't see tools
- Verify config file syntax
- Restart client completely (Cmd+Q on Mac for Cursor)
- Test directly:
npx mcp-local-ragshould run without errors
Contributing
Contributions welcome! See CONTRIBUTING.md for setup and guidelines.
License
MIT License. Free for personal and commercial use.
Blog Posts
- Building a Local RAG for Agentic Coding: Technical deep-dive into the semantic chunking and hybrid search design.
Acknowledgments
Built with Model Context Protocol by Anthropic, LanceDB, and Transformers.js.
Source: README.md at commit bafba24
Tools
0Version history
15- v0.23.2LatestNov 5, 2026
- v0.23.1Oct 22, 2026
- v0.23.0Oct 21, 2026
- v0.22.1Oct 14, 2026
- v0.22.0Oct 14, 2026
- v0.21.3Oct 10, 2026
- v0.21.2Oct 10, 2026
- v0.21.1Oct 7, 2026
- v0.21.0Oct 3, 2026
- v0.20.1Oct 2, 2026
- v0.20.0Oct 1, 2026
- v0.19.1Sep 23, 2026
- v0.19.0Sep 20, 2026
- v0.18.6Sep 18, 2026
- v0.18.5Sep 16, 2026
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