RBT MCP Server
MCP Server for editing RBT documents with partial operations. Reduces token consumption by 80-95% compared to full file read/write operations.
๐ฏ Key Features
Token Optimization: 80-95% reduction in LLM token usage
Structured Operations: Edit specific sections/blocks without loading entire documents
Smart Caching: LRU + TTL cache for frequently accessed documents
TASK Fuzzy Search: Find TASK files by index (e.g., "001" matches "TASK-001-PathResolver.md")
Template-based Creation: Auto-fill placeholders (project-id, feature-id, date)
13 MCP Tools: Complete CRUD operations for RBT documents
๐ฆ Installation
Option 1: Install from source (uv)
Option 2: Direct installation
๐ Quick Start
1. Configure Claude Desktop
Add to ~/Library/Application Support/Claude/claude_desktop_config.json
:
Or use full uv command:
2. Set Environment Variable
3. Test the Server
๐ Available MCP Tools
get_outline - Get document structure (saves 80% tokens)
read_content - Read specific section/block (saves 90% tokens)
update_info - Update status/dependencies
update_section_summary - Update section summary
create_section - Create new sub-section
create_block - Create paragraph/code/list/table
update_block - Update block content
delete_block - Delete block
append_list_item - Add item to list
update_table_row - Update table row
append_table_row - Add table row
create_document - Create from template
clear_cache - Clear document cache
See rbt_mcp_server/README.md for detailed usage.
๐ Token Savings
Operation | Traditional | MCP | Savings |
Read structure | 4,000 | 800 | 80% |
Update status | 8,000 | 300 | 96% |
Add list item | 8,000 | 1,000 | 88% |
Complete TASK | 44,000 | 3,000 | 93% |
๐งช Development
Install development dependencies:
Run tests:
Test coverage:
๐ License
MIT License
๐ค Contributing
Contributions welcome! Please open an issue or submit a pull request.
local-only server
The server can only run on the client's local machine because it depends on local resources.
Tools
Enables efficient editing of RBT documents with structured operations that read and modify specific sections or blocks. Reduces LLM token consumption by 80-95% compared to full file operations through smart caching and partial document access.