micro-rag-mcp 0.1.7

Minimal local RAG MCP server for document folders

micro-rag-mcp: Minimal local RAG MCP server

Minimal, local Retrieval Augmented Generation indexing and search MCP server for folders of documents. Designed for multiple instances via uvx with different data folders. Stores a persistent FAISS index under each data folder in an index directory.

Goals

• Minimal and elegant: a single stdio MCP server process with two tools: search and reindex
• Local only: no network calls; works offline once models are downloaded
• Fast startup: incremental indexing by file mtime with content checksum verification
• Low footprint: sentence-transformers all-MiniLM-L6-v2, FAISS IndexFlatIP, numpy

Default behavior

• Embeddings: sentence-transformers all-MiniLM-L6-v2
• File types: .txt, .md, .pdf, .docx
• Chunking: 1000 characters, 200 overlap, paragraph aware
• Similarity: cosine via L2 normalized vectors and FAISS IndexFlatIP
• Index location: <data_folder>/index
• Startup: scan files and apply incremental changes to the index
• Concurrency: simple file lock index.lock to serialize reindex operations
• Logging: human logs to stderr; tool results are structured

Multi-instance support

• You can run multiple independent MCP server instances by specifying different --title and --inst arguments for each process.
• Both --title and --inst are required.
• The --title is used as the FastMCP server name.
• The --inst is passed verbatim as the FastMCP instructions (can be a sentence, multi-line, etc).
• The default index location remains <data_folder>/index for all instances unless you override with --index-folder.

Example MCP config with two instances

{
  "mcpServers": {
    "team_alpha": {
      "command": "uvx",
      "args": [
        "micro-rag-mcp@0.1.4",
        "--data-folder", "/docs/teamA",
        "--title", "Secret Islands",
        "--inst", "Team Alpha read-only mode with nightly refresh"
      ]
    },
    "team_beta": {
      "command": "uvx",
      "args": [
        "micro-rag-mcp@0.1.4",
        "--data-folder", "/docs/teamB",
        "--title", "Secret Islands",
        "--inst", "Team Beta write-enabled with manual refresh"
      ]
    }
  }
}

• To use multi-line instructions, use \n inside the string:

  • "--inst", "Team Alpha\nRead-only\nRefresh: 02:00 IST"

• Each item in the args array is a single argument; spaces and newlines are preserved as part of the string.

Minimal architecture

Planned source files

• src/micro_rag_mcp/server.py
• src/micro_rag_mcp/index.py
• src/micro_rag_mcp/embedder.py
• src/micro_rag_mcp/ingest.py
• src/micro_rag_mcp/types.py
• src/micro_rag_mcp/utils.py
• src/micro_rag_mcp/main.py
• src/micro_rag_mcp/init.py

Data flow

flowchart LR
  C[Client] --> S[Micro RAG MCP]
  S --> FS[Data folder]
  S --> E[Embedder]
  S --> I[Index manager]
  I --> FI[FAISS index]
  I --> MF[Manifest jsonl]

Startup sequence

sequenceDiagram
  participant C as Client
  participant S as MCP server
  participant I as Index manager
  C->>S: connect via stdio
  S->>I: open or create index
  I->>I: scan data folder and apply incremental changes
  I-->>S: ready with index
  S-->>C: tools ready

Storage layout under <data_folder>/index

• faiss.index: serialized FAISS index
• manifest.jsonl: one JSON record per chunk
• files.json: per-file summary and last processed state
• version: schema version string
• index.lock: lock file during reindex

manifest.jsonl record

{
  "id": 12345,
  "path": "/abs/or/relative/path/to/file.md",
  "ext": ".md",
  "mtime": 1739650000.123,
  "checksum": "sha256:ab...cd",
  "chunk_index": 7,
  "char_start": 4200,
  "char_end": 5200,
  "text": "chunk text...",
  "embedding_dim": 384,
  "deleted": false
}

files.json record

{
  "path": "/abs/or/relative/path/to/file.md",
  "mtime": 1739650000.123,
  "checksum": "sha256:ab...cd",
  "chunk_ids": [1201, 1202, 1203],
  "ext": ".md"
}

Incremental reindex algorithm

• Acquire index.lock; if exists, fail tool call with a clear error
• Load files.json and manifest.jsonl into memory maps
• Walk data folder for allowed extensions
• For each file
  • If new file, ingest, chunk, embed, add vectors and manifest records
  • If mtime differs or checksum differs, delete old chunks, add new chunks
• For files in files.json not present on disk, mark their chunks deleted and optionally reclaim ids later
• Persist FAISS, manifest.jsonl append, files.json
• Release lock and return counts

Search algorithm

• Encode query to embedding
• L2 normalize query and index vectors
• Top k search via FAISS
• For each hit, fetch manifest record, compute snippet, return result

MCP tools

Tool name: search

• Params
  • query: string, required
  • top_k: integer, default 5, min 1, max 50
  • score_threshold: float, default 0.2, 0..1
• Returns
  • results: array of
    • path: string
    • score: float
    • snippet: string
    • chunk_index: integer
    • char_start: integer
    • char_end: integer
    • mtime: float
    • ext: string
• Errors
  • if index missing, server returns empty list and logs a warning

Tool name: reindex

• Params
  • force: boolean, default false
  • path_glob: string, optional shell pattern relative to data folder
• Returns
  • files_added: integer
  • files_updated: integer
  • files_removed: integer
  • chunks_total: integer
  • elapsed_ms: integer
• Errors
  • if indexing already in progress, return a busy error

CLI usage via uvx

• One off run

uvx micro-rag-mcp --data-folder /some/hr_docs --title "HR RAG" --inst "HR instance for confidential search"

• Override defaults

uvx micro-rag-mcp --data-folder /some/docs --index-folder /some/docs/index --exts .txt,.md,.pdf,.docx --title "Docs RAG" --inst "Docs instance"

Local testing before PyPI upload

Before uploading to PyPI, test your package locally to ensure it works correctly with uvx:

1. Development Installation

# Install in development mode for rapid iteration
uv pip install -e .
micro-rag-mcp --help

2. Local uvx Testing

# Test uvx with local package
uvx --from ./ micro-rag-mcp --help

# Create test data and test functionality
mkdir -p test_data
echo "This is a test document about Python programming." > test_data/doc1.txt
echo "Another document about machine learning and AI." > test_data/doc2.md

# Test with real data
uvx --from ./ micro-rag-mcp --data-folder test_data

3. Build and Install Locally

# Build the package
uv build

# Install the built wheel
uv pip install dist/micro_rag_mcp-0.1.0-py3-none-any.whl

# Test uvx with installed package
uvx micro-rag-mcp --help

4. Clean Environment Testing

# Test in isolated virtual environment
uv venv test_env
source test_env/bin/activate
uv pip install dist/micro_rag_mcp-0.1.0-py3-none-any.whl
micro-rag-mcp --help
deactivate

5. Package Validation

# Check package metadata
uv pip show micro-rag-mcp

# Verify entry points
uv pip show -f micro-rag-mcp | grep -A5 "Entry-points"

# Test import
python -c "import micro_rag_mcp; print('Import successful')"

MCP client configuration examples

See the "Multi-instance support" section above for recommended configuration with required --title and --inst arguments.

Dependencies to declare in pyproject.toml

• mcp
• sentence-transformers
• faiss-cpu
• numpy
• pypdf
• python-docx
• pydantic
• charset-normalizer

Implementation notes

• Use stdio transport from the Python mcp package
• Always write human logs to stderr to avoid polluting tool responses
• Normalize embeddings before adding to IndexFlatIP to approximate cosine
• Batch embeddings to manage memory; suggested batch size 64
• For pdf extraction, fall back to text if pages fail; skip images
• For docx, join paragraphs with double newline
• Use UTF-8 with errors replace for text files
• Use SHA256 on normalized text for checksums

Security and privacy

• Operates only on local folders passed explicitly
• No network calls at runtime except model downloads on first use
• No PII leaves the machine

Testing plan

• Create a sample folder with mixed file types
• Run uvx micro-rag-mcp --data-folder sample
• Use search tool from client to validate results and scores
• Modify a file and rerun reindex tool; ensure updates are reflected
• Remove a file and rerun reindex; ensure tombstones are applied

Roadmap

• Background compaction to rebuild faiss and drop tombstones
• Optional rerankers for improved quality
• Additional readers for html and csv
• Hybrid search with BM25 for markdown headings

Repository layout targets

• README.md
• pyproject.toml
• src/micro_rag_mcp/main.py
• src/micro_rag_mcp/server.py
• src/micro_rag_mcp/index.py
• src/micro_rag_mcp/ingest.py
• src/micro_rag_mcp/embedder.py
• src/micro_rag_mcp/types.py
• src/micro_rag_mcp/utils.py

Approval

• If this design matches expectations, the next steps are to update dependencies, scaffold files, and implement the two tools. See the todo list in this workspace.

First-Run and Model Initialization Behavior

• On server startup, the sentence-transformer model is downloaded in the background.
• A background initial reindex is triggered automatically after the model is ready (no need to call reindex manually on first run).
• If a search is called before or during model setup, it will wait up to 5 seconds for the model to become ready. If the model is not ready within 5 seconds, search returns an empty result set (no error).
• Once the model is ready and the background reindex completes, search will return results as expected.
• This ensures that search never fails or blocks indefinitely, even if called immediately after server startup.