FieldCure MCP RAG Server

A Model Context Protocol (MCP) server for indexing and searching local document collections. Supports DOCX, HWPX, PDF (with OCR), Excel, PowerPoint, and audio (Whisper transcription, Windows-only), with hybrid keyword + semantic search optimized for Korean and English.

Built with C# and the official MCP C# SDK.

Commands

fieldcure-mcp-rag
├── serve         --base-path <path>                         # Multi-KB MCP search server (stdio)
├── exec          --path <kb-path> [--force] [--partial ...]  # Headless indexing for a single KB
├── exec-queue    --queue-file <path> [--sweep-all]           # Process deferred indexing queue
├── prune-orphans --base-path <path>                         # Delete orphan KB folders
└── smoke-ocr     --pdf <scanned.pdf>                        # Self-test: OCR a scanned PDF (Windows)

• serve — read-only MCP server serving all knowledge bases under the base path. Single process handles multiple KBs via kb_id parameter. Can run while exec is indexing (SQLite WAL).
• exec — scans source folders, chunks documents, contextualizes with AI, embeds, stores in SQLite. --partial re-runs only downstream stages when models change, preserving OCR output.
• exec-queue — sequential orchestrator consuming a deferred indexing queue. One entry at a time, no GPU contention. --sweep-all processes deferred entries too (used at app shutdown).
• prune-orphans — deletes orphan KB folders (GUID-named, no config.json). Protected folders (., _ prefix, -backup-) are never touched.
• smoke-ocr — diagnostic mode. Loads a scanned PDF through the OCR fallback parser, prints recognized text to stdout, and exits 0 on a non-empty result. Surfaces DllNotFoundException / BadImageFormatException distinctly so a missing or arch-mismatched native is immediately visible. Useful for verifying that the OCR native path is wired correctly on a given host (notably win-arm64 dnx installs).

Features

Search

• Hybrid BM25 + vector search with Reciprocal Rank Fusion (RRF)
• BM25-only fallback when no embedding provider is configured
• Korean-optimized chunking (sentence boundary, decimal protection, parenthesis-aware)
• SIMD-accelerated cosine similarity via System.Numerics.Vector
• FTS5 trigram index for substring and CJK-friendly keyword matching

Indexing

• Incremental indexing with SHA256 change detection
• AI-powered chunk contextualization with bilingual keyword enrichment (see Chunk Contextualization)
• 2-commit pipeline preserves expensive upstream work across embedding failures (see How Indexing Works)
• Math equation extraction from DOCX/HWPX as [math: LaTeX] blocks
• PDF with OCR fallback (Tesseract eng+kor) for scanned pages
• Audio transcription (.mp3, .wav, .m4a, .ogg, .flac, .webm) via Whisper.net — Windows-only. Model size (Tiny→Large) is auto-selected from detected GPU/RAM/cores at startup; each transcript chunk records audio.model_size and audio.transcribed_at for future reindex auditing
• Cross-process indexing lock with stale PID auto-cleanup
• Orphan cleanup for deleted files

Queue Orchestrator

• All indexing requests flow through start_reindex MCP tool — no direct exec spawn
• Scope merge rules: full ⊃ contextualization ⊃ embedding (duplicate requests upgrade, not duplicate)
• PID-based orchestrator lock with reuse defense (orchestrator.lock)
• Logical KB deletion (config.json removal) + prune-orphans physical cleanup
• Deferred indexing for app-shutdown batch processing (--sweep-all)

Operations

• Multi-KB serve: single process serves all knowledge bases under a base path, lazy-loaded per KB
• SQLite WAL mode allows search during indexing
• Graceful shutdown via cancel file
• Per-KB config.json with provider configuration

Integration

• Ollama native — embedding via /api/embed, contextualization via /api/chat with keep_alive and num_ctx support. Requires Ollama 0.4.0+.
• OpenAI-compatible — embedding via /v1/embeddings, contextualization via /v1/chat/completions. Works with OpenAI, Azure OpenAI, Groq, LM Studio, Together AI.
• Gemini native — embedding via /v1beta/models/{model}:embedContent with task_type asymmetric retrieval (RETRIEVAL_DOCUMENT / RETRIEVAL_QUERY) and Matryoshka dimension truncation (768 / 1536 / 3072). gemini-embedding-2, multilingual, 8k token input.
• Anthropic — contextualization via /v1/messages.
• API keys via environment variables — OPENAI_API_KEY, ANTHROPIC_API_KEY, etc. Batch indexing commands (exec, exec-queue) are env-var-only. Interactive MCP search can fall back to MCP elicitation when the client supports it.
• Standard MCP stdio transport (JSON-RPC over stdin/stdout)

Chunk Contextualization

Standard RAG chunking loses context — a sentence about "the protocol" becomes ambiguous when ripped from its surrounding paragraphs. This server addresses that with Unified Chunk Contextualization: a single LLM call per chunk that produces both contextual framing and bilingual (Korean + English) keywords in one pass.

The result is stored alongside the original chunk text:

• Original text is preserved for accurate retrieval display
• Contextualized text is what gets embedded and indexed in BM25
• Bilingual keywords enable cross-lingual search — a Korean query can retrieve English documents and vice versa

This is enabled by setting contextualizer in config.json. It can be disabled (set provider/model to empty) if you prefer raw chunk indexing.

How Indexing Works

The exec command runs a 5-stage pipeline per file:

1. Extract — text from document (DOCX, PDF OCR, audio transcription, etc.)
2. Chunk — split into ~1000 char windows
3. Contextualize — LLM enrichment (optional, see above)
4. Embed — vector embedding via API
5. Persist — save to SQLite

For large files, Stage 1 alone can take 20+ minutes — OCR on a 596-page scanned PDF, or Whisper transcription of a multi-hour audio recording. The first audio file in any KB also pays a one-time ggml model download (cached under {UserProfile}/.fieldcure/whisper-models/). To prevent expensive upstream work from being lost when later stages fail, the pipeline uses a 2-commit model:

Stages 1-3 (Extract → Chunk → Contextualize)
        ↓
[Commit 1] chunks saved as PendingEmbedding
        ↓
Stage 4 (Embed)
   ├─ success → [Commit 2a] promote chunks to Indexed
   └─ failure → chunks remain PendingEmbedding (retry next exec)

Why this matters: A 25-minute OCR result is persisted on disk before any embedding API call. If Stage 4 fails (network error, rate limit, token limit, process crash, even power loss), the chunks survive. The next exec hash-skips the file (no OCR re-run) and the deferred retry pass attempts only Stage 4.

Per-Chunk Failure Isolation (Binary Split)

If a single chunk in a file exceeds the embedding model's token limit (e.g., a math-dense page in a textbook), the binary split algorithm isolates that one chunk:

EmbedBatch([0..1249])         → 400 "input[846] too long"
  ├─ EmbedBatch([0..624])     → OK (promote 625)
  └─ EmbedBatch([625..1249])  → 400
      ├─ EmbedBatch([625..937])  → 400
      │   ... (binary search narrows toward chunk 846)
      │   └─ EmbedBatch([846..846]) → 400 (mark chunk 846 Failed)
      └─ EmbedBatch([938..1249]) → OK (promote 312)

Result: 1249 chunks indexed, only chunk 846 marked Failed. The file's status becomes Degraded — partially searchable instead of completely missing.

Deferred Retry Pass

Each exec ends with a retry pass over any chunks left in PendingEmbedding state from previous runs:

• Reads enriched text from DB — no OCR or contextualization re-run
• Calls the embedding API only — typically seconds, not minutes
• Up to 3 retries per chunk; on exhaustion, the chunk is marked Failed
• Auth errors (401/403) flag the provider as unavailable and skip the rest of the pass

File States

Schema Versioning

Each KB DB carries a PRAGMA user_version tag. The exec command migrates older schemas automatically as part of InitializeSchema(). The serve command opens DBs read-only and never triggers migration — older-schema KBs continue to serve search queries correctly while their new-feature columns remain unused.

Installation

dotnet tool (recommended)

dotnet tool install -g FieldCure.Mcp.Rag

From source

git clone https://github.com/fieldcure/fieldcure-mcp-rag.git
cd fieldcure-mcp-rag
dotnet build

Requirements

• .NET 8.0 Runtime or later
• OCR: Windows x64 only — Tesseract OCR for scanned PDFs loads lazily on first use (Windows only). On other platforms, PDFs with embedded text work normally; scanned pages without a text layer are silently skipped.
• An embedding provider (Ollama, OpenAI, etc.) — optional, BM25 search works without it
• Ollama 0.4.0 or later (if using Ollama for embedding or contextualization)

Quick Start

Index a folder and search it without any embedding setup (BM25 only):

# 1. Install
dotnet tool install -g FieldCure.Mcp.Rag

# 2. Create a minimal config
$kbPath = "$env:LOCALAPPDATA\FieldCure\Mcp.Rag\demo"
New-Item -ItemType Directory -Force -Path $kbPath
@'
{
  "id": "demo",
  "name": "Demo KB",
  "sourcePaths": ["C:\\my-docs"]
}
'@ | Set-Content "$kbPath\config.json"

# 3. Index
fieldcure-mcp-rag exec --path $kbPath

# 4. Start the search server
fieldcure-mcp-rag serve --base-path "$env:LOCALAPPDATA\FieldCure\Mcp.Rag"

For full retrieval quality with semantic search and contextualization, add embedding and contextualizer blocks to config.json — see Usage below.

Usage

1. Create a knowledge base folder

%LOCALAPPDATA%\FieldCure\Mcp.Rag\{kb-id}\config.json

{
  "id": "my-kb-001",
  "name": "Project Docs",
  "created": "2026-04-03T00:00:00Z",
  "sourcePaths": ["C:\\Users\\me\\Documents\\project-docs"],
  "contextualizer": {
    "provider": "anthropic",
    "model": "claude-haiku-4-5-20251001",
    "apiKeyPreset": "Claude"
  },
  "embedding": {
    "provider": "openai",
    "model": "text-embedding-3-small",
    "apiKeyPreset": "OpenAI"
  }
}

API keys are resolved from environment variables: apiKeyPreset: "OpenAI" → OPENAI_API_KEY, "Claude" → ANTHROPIC_API_KEY, "Gemini" (or "Google") → GEMINI_API_KEY.

Gemini embedding example — asymmetric retrieval with 1536-dim Matryoshka truncation (50% storage of full 3072 with identical MTEB score):

"embedding": {
  "provider": "gemini",
  "model": "gemini-embedding-2",
  "apiKeyPreset": "Gemini",
  "dimension": 1536
}

2. Index documents

fieldcure-mcp-rag exec --path "C:\Users\me\AppData\Local\FieldCure\Mcp.Rag\my-kb-001"

3. Start MCP search server

fieldcure-mcp-rag serve --base-path "C:\Users\me\AppData\Local\FieldCure\Mcp.Rag"

A single serve process handles all knowledge bases under the base path. Tools accept a kb_id parameter to target a specific KB.

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "rag": {
      "command": "fieldcure-mcp-rag",
      "args": ["serve", "--base-path", "C:\\Users\\me\\AppData\\Local\\FieldCure\\Mcp.Rag"],
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "ANTHROPIC_API_KEY": "sk-ant-..."
      }
    }
  }
}

config.json Reference

Tools

All tools (except list_knowledge_bases) require a kb_id parameter to specify the target knowledge base.

Search Modes

Supported Formats

Document formats are provided by FieldCure.DocumentParsers:

• DOCX — Microsoft Word (with math equation extraction)
• HWPX — Korean standard document (OWPML, with math equation extraction)
• XLSX — Excel spreadsheets
• PPTX — PowerPoint presentations
• PDF — PDF text extraction with ## Page N headers; OCR fallback for scanned pages (Tesseract, eng+kor)
• TXT, MD — Plain text / Markdown

Project Structure

src/FieldCure.Mcp.Rag/
├── Program.cs                     # CLI entry (exec | exec-queue | serve | prune-orphans)
├── MultiKbContext.cs              # Multi-KB manager (lazy load, Classify, lazy unload)
├── ExecQueueRunner.cs             # Deferred queue orchestrator
├── OrphanCleanupRunner.cs         # prune-orphans CLI
├── Configuration/
│   ├── RagConfig.cs               # config.json model (KeepAlive, NumCtx fields)
│   └── OllamaDefaults.cs          # Shared defaults (KeepAlive="5m", NumCtx=8192)
├── Indexing/
│   ├── IndexingEngine.cs          # 5-stage pipeline (2-commit model)
│   └── EmbeddingBatchSplitter.cs  # Binary-split per-chunk failure isolation
├── Contextualization/
│   ├── IChunkContextualizer.cs
│   ├── OpenAiChunkContextualizer.cs   # /v1/chat/completions
│   ├── OllamaChunkContextualizer.cs   # /api/chat (keep_alive + num_ctx)
│   ├── AnthropicChunkContextualizer.cs
│   └── NullChunkContextualizer.cs
├── Embedding/
│   ├── IEmbeddingProvider.cs
│   ├── OpenAiCompatibleEmbeddingProvider.cs  # /v1/embeddings
│   ├── OllamaEmbeddingProvider.cs            # /api/embed (keep_alive)
│   ├── NullEmbeddingProvider.cs
│   └── EmbeddingBatchSizes.cs
├── Storage/
│   └── SqliteVectorStore.cs       # SQLite + FTS5 + SIMD cosine similarity
├── Search/
│   ├── HybridSearcher.cs          # BM25 + Vector → RRF
│   └── RrfFusion.cs
├── Chunking/
│   ├── TextChunker.cs
│   └── ChunkLimits.cs
└── Tools/
    ├── ListKnowledgeBasesTool.cs
    ├── SearchDocumentsTool.cs
    ├── GetDocumentChunkTool.cs
    ├── StartReindexTool.cs        # Queue entry point + orchestrator spawn
    ├── CancelReindexTool.cs       # Remove pending queue entry
    ├── GetIndexInfoTool.cs        # Includes queue state
    └── CheckChangesTool.cs

Data Storage

Knowledge base data is stored at %LOCALAPPDATA%\FieldCure\Mcp.Rag\{kb-id}\:

• config.json — knowledge base configuration
• rag.db — SQLite database (chunks, embeddings, FTS5 index, file hashes, indexing lock)

Queue and lock files at %LOCALAPPDATA%\FieldCure\Mcp.Rag\:

• .deferred-queue.json — pending indexing requests
• orchestrator.lock — PID lock for the queue orchestrator

Development

# Build
dotnet build

# Test
dotnet test

# Pack as dotnet tool
dotnet pack src/FieldCure.Mcp.Rag -c Release

See Also

Part of the AssistStudio ecosystem.

License

MIT