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.

Features

• Runs locally — document parsing, embeddings, storage, and search run on your machine. After the initial model download, text ingestion and search work offline.
• Hybrid search — semantic retrieval finds related concepts, while keyword matching boosts exact technical terms.
• Semantic chunking — documents are split at topic boundaries instead of fixed character counts. Markdown code blocks stay intact.
• MCP and CLI — use the same index from an AI coding tool or directly from the terminal.

No API key, Docker, Python, or external database is required.

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.

mcp-local-rag uses the standard MCP protocol over a local stdio server, so it works with AI coding tools and other MCP hosts that support local MCP servers.

Use one of the examples below, or register npx -y mcp-local-rag and set BASE_DIR using your client's MCP configuration format.

For 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

For 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"

For 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"
      }
    }
  }
}

For 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.

Why This Exists

Some document sets cannot be sent to a hosted embedding service because of confidentiality or organizational policy. Keeping the index local makes them searchable without adding a per-query API cost.

Semantic search alone can miss exact identifiers that matter in technical documentation. Keyword reranking keeps those terms visible without giving up natural-language retrieval.

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.

MCP Tools

Syncing a Document Root

sync_start ingests new and changed files, skips byte-identical files, and removes index entries for files that no longer exist:

Sync everything under the configured document roots and wait for completion.

The tool returns a jobId immediately. Clients should poll sync_status until its state becomes succeeded or failed. There is no visual mode during sync; changed PDFs are ingested as text.

Only one sync job is retained by the server process. A newer job replaces a finished record, and restarting the server discards it.

Ingesting One File

ingest_file accepts PDF, DOCX, TXT, and Markdown. MCP file paths must be absolute and must stay inside a configured document root:

Ingest the document at /Users/me/docs/api-spec.pdf.

Re-ingesting the same path replaces its existing chunks.

Searching and Reading More Context

What does the API documentation say about authentication?
Find the documented behavior of ERR_CONNECTION_REFUSED.

Results contain the text, source path, title, chunk index, and relevance score. Pass the chunkIndex and either filePath or source from a result to read_chunk_neighbors when the answer needs more context:

Read the surrounding chunks for that authentication result.

Both query_documents and list_files accept an optional absolute scope path prefix, or a list of prefixes. A prefix matches the exact path and its descendants.

Ingesting HTML

Use ingest_data after the MCP client fetches a page:

Fetch https://example.com/docs and ingest the HTML.

The server extracts the main article, converts it to Markdown, and stores it under the supplied source identifier. Reusing the same source updates the existing content.

Respect the source site's terms and copyright when indexing external content.

PDF Figures

Visual mode adds a generated caption for figure-heavy PDF pages. It is opt-in and does not load a vision model during normal ingestion.

Ingest /Users/me/docs/research-paper.pdf with visual: true.

npx mcp-local-rag ingest ./docs/research-paper.pdf --visual

Select the larger model with visualQuality: "quality" over MCP or --visual-quality quality over CLI. Measured CPU inference was about twice as slow as fast, though results depend on hardware and model updates.

Captions are auxiliary text, not faithful transcriptions. Treat retrieved captions and document text as untrusted input rather than instructions.

CLI

The CLI uses the same parser, embedder, and vector store without an MCP client:

npx mcp-local-rag ingest ./docs/
npx mcp-local-rag sync ./docs/
npx mcp-local-rag query "authentication API"
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"

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.

The CLI does not read MCP client configuration. Set the same environment variables or flags if both interfaces should share an index. In particular, MODEL_NAME and the CLI --model-name must match for a shared database.

Search Tuning

Keyword boost is enabled by default. Relevance-gap grouping and the distance and file filters are optional controls for corpora that need tighter result selection.

For API specifications and other documents containing many identifiers, a stronger keyword weight can improve exact-term ranking:

"env": {
  "RAG_HYBRID_WEIGHT": "0.7"
}

• 0.7 — slightly stronger exact-term reranking than the default
• 1.0 — maximum keyword boost

How It Works

During ingestion:

1. The parser extracts text for the input format.
2. The semantic chunker finds topic boundaries and preserves Markdown code blocks.
3. Transformers.js creates embeddings locally.
4. LanceDB stores the chunks, metadata, vectors, and full-text index.

During search:

1. The query is embedded with the same model.
2. Vector search retrieves semantically related chunks.
3. Optional distance and relevance-group filters narrow the candidates when configured.
4. Full-text matches boost exact query terms.

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.

Configuration

The MCP server reads environment variables. The CLI accepts the same variables plus the listed flags, with CLI flags taking precedence.

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