MCP Local RAG

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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 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:
claude mcp add local-rag --scope user --env BASE_DIR=/absolute/path/to/your/documents -- npx -y mcp-local-rag
Codex
Add to ~/.codex/config.toml:
[mcp_servers.local-rag]
command = "npx"
args = ["-y", "mcp-local-rag"]
[mcp_servers.local-rag.env]
BASE_DIR = "/absolute/path/to/your/documents"
OpenCode
Add to ~/.config/opencode/opencode.json (or opencode.jsonc):
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"local-rag": {
"type": "local",
"command": ["npx", "-y", "mcp-local-rag"],
"environment": {
"BASE_DIR": "/absolute/path/to/your/documents"
}
}
}
}
Cursor
Add to ~/.cursor/mcp.json:
{
"mcpServers": {
"local-rag": {
"command": "npx",
"args": ["-y", "mcp-local-rag"],
"env": {
"BASE_DIR": "/absolute/path/to/your/documents"
}
}
}
}
Restart the client, then ask it to build the index:
Sync all documents in the configured root and wait until it finishes.
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:
What does the API documentation say about authentication?
CLI Quick Start
To use the CLI without an MCP client:
npx mcp-local-rag ingest ./docs/
npx mcp-local-rag query "authentication API"
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:
Find the documented behavior of ERR_CONNECTION_REFUSED.
Read the surrounding chunks for that result.
You can also ingest a single file or HTML already fetched by the client. Reusing the same path
or source updates the existing entry. MCP file paths must be absolute and inside a configured
document root.
Source context can include headings, original-file line numbers for MD/TXT, and page numbers
for PDFs. PDF heading detection can miss headings or mistake body text for a heading. Re-ingest
documents indexed before v0.21.0 to add source context; sync skips unchanged files.
MCP Tools
CLI
Use the CLI to update the index, narrow searches, or remove indexed content:
npx mcp-local-rag sync ./docs/
npx mcp-local-rag query "auth" --scope /docs/api --scope /docs/guide
npx mcp-local-rag read-neighbors --file-path /abs/path.md --chunk-index 5
npx mcp-local-rag list
npx mcp-local-rag status
npx mcp-local-rag delete ./docs/old.pdf
npx mcp-local-rag delete --source "https://example.com/docs"
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:
npx mcp-local-rag --db-path ./my-db query "authentication"
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.
Agent Skills
Agent Skills provide query and ingestion guidance for AI assistants:
npx mcp-local-rag skills install --claude-code
npx mcp-local-rag skills install --claude-code --global
npx mcp-local-rag skills install --codex
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.
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.
{
"env": {
"RAG_RERANK_CMD": "/path/to/reranker --query {query} --top {top}",
"RAG_RERANK_TIMEOUT_MS": "10000"
}
}
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.
npx mcp-local-rag ingest ./docs/paper.pdf --visual --visual-quality quality
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.
npx mcp-local-rag ingest ./docs/paper.pdf --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-dir roots.
- 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_CMD names 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.
- Back up an index by copying its
DB_PATH directory 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)
- Verify config file syntax
- Restart client completely (Cmd+Q on Mac for Cursor)
- Test directly:
npx mcp-local-rag should run without errors
Contributing
Contributions welcome! See CONTRIBUTING.md for setup and guidelines.
License
MIT License. Free for personal and commercial use.
Blog Posts
Acknowledgments
Built with Model Context Protocol by Anthropic,
LanceDB, and
Transformers.js.