rbt-mcp-server 0.2.1

MCP Server for editing RBT documents with partial operations. Reduces token consumption by 80-95%.

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（5 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
• 5 Graphiti Tools: add_memory, search_nodes, search_facts, get_episodes, delete_episode

📦 Installation

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

Note:

• NEO4J_URI, NEO4J_USER, NEO4J_PASSWORD, OPENAI_API_KEY are required for Graphiti tools

Or use full uv command:

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

2. Set Environment Variables

# 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

Graphiti Memory Tools

1. search_memory_nodes - Search knowledge graph nodes (entities, preferences, procedures)
2. search_memory_facts - Search knowledge graph facts (relationships)
3. get_episodes - Retrieve recent memory episodes
4. delete_episode - Delete specific episode
5. delete_entity_edge - Delete entity relationship

🔗 Graphiti Integration Usage

Adding Documents to Knowledge Graph

# The add_memory tool automatically chunks documents
await add_memory(
    name="TASK-001-PathResolver",
    episode_body="<document content>",
    group_id="knowledge-smith",
    source="text"
)

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.

🧪 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.