# rag [Health: Active]

**Category:** 🔎 Search & Data Extraction  
**Repository:** https://github.com/fieldcure/fieldcure-mcp-rag  
**GitHub Stars:** 0  
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**Directory Page:** https://allmcps.com/mcp/rag

## Description
MCP RAG server with hybrid search, multi-KB support, and AI-powered chunk contextualization.

## Tools
Capabilities this server exposes over MCP:

- **list_knowledge_bases** — List all available KBs with status (file/chunk counts, indexing status)
- **search_documents** — Hybrid BM25 + vector search with RRF. Supports `search_mode`: `auto`, `bm25`, `vector
- **get_document_chunk** — Retrieve full content of a specific chunk by ID
- **start_reindex** — Queue an indexing request. Scope merge, force/deferred flags, orchestrator auto-spawn
- **cancel_reindex** — Remove a pending (not-yet-started) queue entry
- **get_index_info** — Index metadata, queue state (status/position/deferred/last_error), contextualization health
- **check_changes** — Dry-run filesystem scan. Lightweight, no API calls

## Claude Desktop Quick Installation
No confirmed install details yet, so no config is published. Follow the setup steps in the repository above.

## Documentation

## What the rag MCP server does

The rag MCP server indexes local document collections and makes them searchable through MCP. It supports DOCX, HWPX, PDF, Excel, PowerPoint, and audio files. Scanned PDFs can use Tesseract OCR, while audio transcription uses Whisper.net on Windows. The resulting knowledge bases are stored locally and can be served together from one base path.

Search combines BM25 keyword matching and vector similarity with Reciprocal Rank Fusion. It also supports BM25-only operation when no embedding provider is configured. Korean-oriented chunking, FTS5 trigram matching, and optional bilingual enrichment help with Korean, English, CJK, substring, and cross-language queries.

## How the rag MCP server works

The indexing pipeline extracts content, divides it into roughly 1,000-character chunks, optionally contextualizes each chunk with an LLM, generates embeddings, and writes the results to SQLite. Original chunk text remains available for display, while contextualized text can be used for indexing and embedding. Hash-based change detection avoids repeating work for unchanged files.

A two-commit process saves extracted and enriched chunks before embedding completes. If an embedding request fails, later retries can use the saved chunks without repeating OCR, transcription, or contextualization. Individual oversized or invalid chunks can be isolated, allowing other chunks from the same file to remain searchable.

The MCP service uses SQLite WAL mode, so searches can continue while indexing runs. Reindex requests are queued and merged by scope; a separate queue executor processes deferred work sequentially to avoid concurrent indexing contention.

## Setup and configuration

Run the `serve` command with a base path containing knowledge bases. The service uses standard MCP stdio transport and accepts a `kb_id` parameter for multi-KB operations. Each knowledge base has a `config.json` containing provider settings.

Embedding and contextualization can use Ollama, OpenAI-compatible endpoints, Gemini, or Anthropic for the supported roles. OpenAI-compatible configurations can target OpenAI, Azure OpenAI, Groq, LM Studio, or Together AI. Provider credentials are supplied through environment variables such as `OPENAI_API_KEY` or `ANTHROPIC_API_KEY`; the exact variables depend on the configured provider. Batch commands use environment variables, while interactive MCP search may use MCP elicitation when the client supports it.

Use `exec` for headless indexing of one knowledge base, `exec-queue` for deferred queue processing, `prune-orphans` to remove unconfigured knowledge-base folders, and `smoke-ocr` to test scanned-PDF OCR on Windows.

## Tools and capabilities

The rag MCP server exposes these MCP tools:

- `list_knowledge_bases` reports available knowledge bases, file and chunk counts, and indexing status.
- `search_documents` runs automatic, BM25, or vector search.
- `get_document_chunk` returns the complete content for a chunk ID.
- `start_reindex` queues indexing with merge, force, and deferred options.
- `cancel_reindex` removes a queued request that has not started.
- `get_index_info` reports index metadata, queue position and status, errors, and contextualization health.
- `check_changes` performs a lightweight filesystem scan without API calls.

## Limitations and notes

Audio transcription is Windows-only, and OCR depends on the native OCR setup. Large scanned documents and long recordings may take substantial time during extraction; the first audio file may also require a one-time Whisper model download. The contextualization stage is optional, but embeddings require a configured provider for vector search.

The service is read-only at the MCP serving layer; indexing is managed through the command-line pipeline and queued reindex operations. Failed or deferred chunks may leave a knowledge base in a partial or degraded state until retries succeed. The project is distributed under the MIT license.

_Full upstream README: https://allmcps.com/mcp/rag/readme_