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dl1683

ickyMCP

by dl1683
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Python
Local

ickyMCP

RAG MCP Server for Document Search. Built for legal professionals and business users who need to search across large document collections.

Features

  • Semantic Search: Find relevant content based on meaning, not just keywords

  • Document Support: PDF, Word (.docx), PowerPoint (.pptx), Excel (.xlsx), Markdown, Text

  • 4K Token Chunks: Large chunks preserve context for legal and business documents

  • Incremental Indexing: Only re-index changed files

  • Local Embeddings: Uses nomic-embed-text-v1.5 (no API costs)

  • SQLite Storage: Single portable database file

Installation

# Clone or copy the project cd ickyMCP # Create virtual environment python -m venv venv source venv/bin/activate # or `venv\Scripts\activate` on Windows # Install dependencies pip install -r requirements.txt # Or install as package pip install -e .

Configuration

Environment Variables

Variable

Default

Description

ICKY_CHUNK_SIZE

4000

Tokens per chunk

ICKY_CHUNK_OVERLAP

500

Overlap between chunks

ICKY_DB_PATH

./icky.db

Path to SQLite database

ICKY_EMBEDDING_MODEL

nomic-ai/nomic-embed-text-v1.5

Embedding model

Claude Code Configuration

Add to your claude_desktop_config.json or MCP settings:

{ "mcpServers": { "ickyMCP": { "command": "python", "args": ["/path/to/ickyMCP/run.py"], "env": { "ICKY_CHUNK_SIZE": "4000", "ICKY_CHUNK_OVERLAP": "500", "ICKY_DB_PATH": "/path/to/icky.db" } } } }

Usage

Tools Available

index

Index documents from a file or directory.

index(path="/contracts/2024", patterns=["*.pdf", "*.docx"])

search

Semantic search across indexed documents.

search(query="indemnification clause", top_k=10, file_types=["pdf"])

similar

Find chunks similar to a given text.

similar(chunk_text="The parties agree to...", top_k=5)

refresh

Re-index only files that have changed.

refresh(path="/contracts")

list

List all indexed documents.

list(path_filter="/contracts")

delete

Remove documents from the index.

delete(path="/contracts/old") delete(all=true) # Clear entire index

status

Get server status and statistics.

status()

How It Works

  1. Indexing: Documents are parsed, split into 4K token chunks with 500 token overlap

  2. Embedding: Each chunk is embedded using nomic-embed-text-v1.5 (768 dimensions)

  3. Storage: Embeddings stored in SQLite with sqlite-vec for fast vector search

  4. Search: Query is embedded, compared against all chunks using cosine similarity

  5. Results: Top-K most similar chunks returned with full text and metadata

System Requirements

  • Python 3.10+

  • 4GB RAM (2GB for model + headroom)

  • ~1GB disk space (model + database)

License

MIT

This server cannot be installed

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security - not tested
F
license - not found
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quality - not tested

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