mcp-local-rag

MCP Local RAG

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.

Run this command:

claude mcp add local-rag --scope user --env BASE_DIR=/absolute/path/to/your/documents -- npx -y mcp-local-rag

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"

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"
      }
    }
  }
}

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

Input	How to ingest
PDF, DOCX, TXT, Markdown	File ingestion or directory sync
HTML already fetched by the client	ingest_data
Plain text or Markdown held in memory	ingest_data with a stable source identifier

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. 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.

Tool	Purpose
sync_start	Reconcile the index with all configured roots or one path
sync_status	Poll a running sync job
ingest_file	Ingest or replace one file
ingest_data	Ingest text, Markdown, or HTML already held by the client
query_documents	Search with semantic matching and keyword boost
read_chunk_neighbors	Read surrounding chunks from a search result
list_files	Show supported files and their ingestion state
delete_file	Delete an indexed file or an ingest_data item
status	Show index and search status

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.

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:

npx mcp-local-rag --db-path /new/project/lancedb relocate --from /old/project --to /new/project

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:

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.

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.

Environment Variable	CLI Flag	Default	Description
BASE_DIR	--base-dir	Current directory	One document root; the CLI flag is repeatable on ingest, list, and sync
BASE_DIRS	N/A	(unset)	JSON array of document roots; takes precedence over BASE_DIR
IGNORE_PATHS	N/A	(unset)	JSON array of glob patterns for files to keep out of the index
DB_PATH	--db-path	./lancedb/	Vector database location
CACHE_DIR	--cache-dir	./models/	Model cache directory
HF_ENDPOINT	N/A	https://huggingface.co	Hugging Face model download endpoint; use a mirror URL when direct downloads are blocked
MAX_FILE_SIZE	--max-file-size	104857600 (100MB)	Maximum file size in bytes

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.

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.

Environment Variable	CLI Flag	Default	Description
MODEL_NAME	--model-name	Xenova/all-MiniLM-L6-v2	Hugging Face embedding model
CHUNK_MIN_LENGTH	--chunk-min-length	50	Minimum length in characters (1–10000) for ordinary chunks; a fragment of content split to fit the model's token limit can be shorter
EMBED_TITLE_PREFIX	N/A	false	Add the document title to each chunk's embedding input
EMBED_HEADING_PREFIX	N/A	false	Add the heading hierarchy to each chunk's embedding input when it fits
RAG_DEVICE	N/A	cpu	ONNX Runtime execution device
RAG_DTYPE	N/A	fp32	Embedding dtype passed to the selected model

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.

Variable	Default	Description
RAG_HYBRID_WEIGHT	0.6	Keyword boost factor (0.0–1.0). 0 disables keyword reranking; 1 applies the maximum boost.
RAG_GROUPING	(not set)	similar keeps the first relevance group; related keeps up to two, using significant vector-distance gaps as boundaries.
RAG_MAX_DISTANCE	(not set)	Filter out low-relevance results (e.g., 0.5).
RAG_MAX_FILES	(not set)	Limit results to top N files (e.g., 1 for single best file).
RAG_RERANK_CMD	(not set)	MCP only: external command; {query} passes the query and {top} the requested result count.
RAG_RERANK_TIMEOUT_MS	10000	Time budget per rerank call in milliseconds (100–600000).

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.

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.
• 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_PATH directory while no writer is active.

"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

1. Verify config file syntax
2. Restart client completely (Cmd+Q on Mac for Cursor)
3. 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

• 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.