KnowledgeSmith MCP Server

MCP Server for Graphiti memory and document chunking. Previously included RBT document editing tools (now archived).

📦 Archive Notice

RBT Document Editor Tools (Archived 2025-10-09)

The RBT document editing功能已於 2025-10-09 封存，改用原生 Claude Code Read/Edit/Write 工具以降低維護成本和 token 使用。

封存內容：

document_service.py - 文件服務
document_parser.py - 文件解析器
11 個 editor MCP 工具（get_outline, read_content, update_block 等）
templates/ - 文件模板
cache.py - 文件快取

保留功能：

✅ chunking/ - 文件分塊與同步功能
✅ graphiti_tools.py - Graphiti 記憶體功能（8 個工具）

如何恢復封存的代碼：

# 查看封存版本 git show v-with-editor # 恢復特定檔案 git checkout v-with-editor -- rbt_mcp_server/document_service.py # 或建立分支使用完整封存版本 git checkout -b restore-editor v-with-editor

🎯 Current Features

Graphiti Knowledge Graph Integration

Intelligent Chunking: Automatically split documents into semantic chunks based on document structure (sections for RBT, H3 headings for Markdown)
Incremental Sync: Only update changed chunks, preserving unchanged content
Neo4j Backend: Store document chunks as episodes in Graphiti knowledge graph
graphiti-memory Compatible: Drop-in replacement with same search_nodes/search_facts API
8 MCP Tools: add_document, search_memory_nodes, search_memory_facts, get_episodes, delete_episode, get_entity_edge, delete_entity_edge, clear_graph

📦 Installation

Prerequisites

1. Setup Neo4j Database

Using Docker (recommended):

docker run \ -p 7474:7474 \ -p 7687:7687 \ -e NEO4J_AUTH=neo4j/your-password \ --name neo4j \ neo4j:latest

Verify at: http://localhost:7474

2. Get OpenAI API Key

Required for Graphiti embeddings and graph operations.

Install MCP Server

Option 1: Install from source (uv)

# Clone repository git clone https://github.com/yourusername/KnowledgeSmith.git cd KnowledgeSmith # Install with uv uv pip install -e .

Option 2: Direct installation

uv pip install rbt-mcp-server

🚀 Quick Start

1. Configure Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{ "mcpServers": { "graphiti-memory-server": { "type": "stdio", "command": "rbt-mcp-server", "env": { "RBT_ROOT_DIR": "/path/to/your/document/root", "NEO4J_URI": "bolt://localhost:7687", "NEO4J_USER": "neo4j", "NEO4J_PASSWORD": "your-password", "OPENAI_API_KEY": "your-openai-api-key" } } } }

Required Environment Variables:

RBT_ROOT_DIR: Root directory for document comparison (required for add_document tool)
NEO4J_URI, NEO4J_USER, NEO4J_PASSWORD: Neo4j database connection
OPENAI_API_KEY: OpenAI API key for Graphiti embeddings

Or use full uv command:

{ "mcpServers": { "graphiti-memory-server": { "type": "stdio", "command": "uv", "args": ["run", "rbt-mcp-server"], "env": { "RBT_ROOT_DIR": "/path/to/your/document/root", "NEO4J_URI": "bolt://localhost:7687", "NEO4J_USER": "neo4j", "NEO4J_PASSWORD": "your-password", "OPENAI_API_KEY": "your-openai-api-key" } } } }

2. Set Environment Variables (Optional - if not using Claude Desktop)

# Required for add_document tool export RBT_ROOT_DIR=/path/to/your/document/root # Required for Graphiti integration export NEO4J_URI=bolt://localhost:7687 export NEO4J_USER=neo4j export NEO4J_PASSWORD=your-password export OPENAI_API_KEY=your-openai-api-key

3. Test the Server

rbt-mcp-server

📚 Available MCP Tools

Document Management

add_document - Sync documents to knowledge graph with automatic chunking
- Supports Markdown (chunked by H3 headings) and RBT documents
- Incremental sync: only updates changed chunks

Knowledge Graph Query

search_memory_nodes - Search knowledge graph nodes (entities, preferences, procedures)
search_memory_facts - Search knowledge graph facts (relationships)
get_episodes - Retrieve recent memory episodes

Data Management

delete_episode - Delete specific episode
get_entity_edge - Get entity relationship edge by UUID
delete_entity_edge - Delete entity relationship edge
clear_graph - Clear all data from knowledge graph (⚠️ irreversible)

🔗 Graphiti Integration Usage

Adding Documents to Knowledge Graph

General Markdown Documents:

add_document( new_file_path="/absolute/path/to/document.md", project_id="my-project", file_path="docs/guide.md" # relative path for general docs )

RBT Documents (REQ/BP/TASK):

add_document( new_file_path="/absolute/path/to/TASK-001.md", project_id="knowledge-smith", feature_id="my-feature", rbt_type="TASK", file_path="001" # task number for TASK documents )

Searching Knowledge

# Search for nodes (entities, preferences, procedures) results = await search_nodes( query="documentation preferences", group_ids=["knowledge-smith"], entity="Preference", max_nodes=10 ) # Search for facts (relationships) facts = await search_facts( query="task dependencies", group_ids=["knowledge-smith"], max_facts=10 )

Difference from graphiti-memory MCP

This MCP server extends the original graphiti-memory MCP with document chunking capabilities:

Original graphiti-memory: Stores entire documents as single episodes
This MCP (graphiti-chunk-mcp): Automatically chunks documents into semantic sections
- RBT documents: Split by section (sec-*)
- Markdown documents: Split by H3 headings (###)
- Incremental updates: Only sync changed chunks

API Compatibility: All search_nodes, search_facts, get_episodes functions maintain the same interface as graphiti-memory.

📖 Documentation

Markdown Writing Guide - How to write Markdown documents that work well with the chunker
MCP Installation Guide - Detailed installation and usage instructions

🧪 Development

Install development dependencies:

uv sync --dev

Run tests:

RBT_ROOT_DIR=/test/root uv run pytest -v

Test coverage:

RBT_ROOT_DIR=/test/root uv run pytest --cov=rbt_mcp_server --cov-report=html

📝 License

MIT License

🤝 Contributing

Contributions welcome! Please open an issue or submit a pull request.

RBT Document Editor