Recursive Companion MCP
An MCP server that implements iterative refinement of responses through self-critique cycles, breaking the process into discrete steps to avoid timeouts and show progress.
README Documentation
Recursive Companion MCP
An MCP (Model Context Protocol) server that implements iterative refinement through self-critique cycles. Inspired by Hank Besser's recursive-companion, this implementation adds incremental processing to avoid timeouts and enable progress visibility.
Features
- Incremental Refinement: Avoids timeouts by breaking refinement into discrete steps
- Mathematical Convergence: Uses cosine similarity to measure when refinement is complete
- Domain-Specific Optimization: Auto-detects and optimizes for technical, marketing, strategy, legal, and financial domains
- Progress Visibility: Each step returns immediately, allowing UI updates
- Parallel Sessions: Support for multiple concurrent refinement sessions
- Auto Session Tracking: No manual session ID management needed
- AI-Friendly Error Handling: Actionable diagnostics and recovery hints for AI assistants
How It Works
The refinement process follows a Draft → Critique → Revise → Converge pattern:
- Draft: Generate initial response
- Critique: Create multiple parallel critiques (using faster models)
- Revise: Synthesize critiques into improved version
- Converge: Measure similarity and repeat until threshold reached
Installation
Prerequisites
- Python 3.10+
- uv package manager
- AWS Account with Bedrock access
- Claude Desktop app
Setup
- Clone the repository:
git clone https://github.com/yourusername/recursive-companion-mcp.git
cd recursive-companion-mcp
- Install dependencies:
uv sync
-
Configure AWS credentials as environment variables or through AWS CLI
-
Add to Claude Desktop config (
~/Library/Application Support/Claude/claude_desktop_config.json
):
{
"mcpServers": {
"recursive-companion": {
"command": "/path/to/recursive-companion-mcp/run_server.sh",
"env": {
"AWS_REGION": "us-east-1",
"AWS_ACCESS_KEY_ID": "your-key",
"AWS_SECRET_ACCESS_KEY": "your-secret",
"BEDROCK_MODEL_ID": "anthropic.claude-3-sonnet-20240229-v1:0",
"CRITIQUE_MODEL_ID": "anthropic.claude-3-haiku-20240307-v1:0",
"CONVERGENCE_THRESHOLD": "0.95",
"PARALLEL_CRITIQUES": "2",
"MAX_ITERATIONS": "5",
"REQUEST_TIMEOUT": "600"
}
}
}
}
Usage
The tool provides several MCP endpoints:
Start a refinement session
Use start_refinement to refine: "Explain the key principles of secure API design"
Continue refinement step by step
Use continue_refinement with session_id "abc123..."
Get final result
Use get_final_result with session_id "abc123..."
Other tools
get_refinement_status
- Check progress without advancinglist_refinement_sessions
- See all active sessions
Configuration
Environment Variable | Default | Description |
---|---|---|
BEDROCK_MODEL_ID | anthropic.claude-3-sonnet-20240229-v1:0 | Main generation model |
CRITIQUE_MODEL_ID | Same as BEDROCK_MODEL_ID | Model for critiques (use Haiku for speed) |
CONVERGENCE_THRESHOLD | 0.98 | Similarity threshold for convergence (0.90-0.99) |
PARALLEL_CRITIQUES | 3 | Number of parallel critiques per iteration |
MAX_ITERATIONS | 10 | Maximum refinement iterations |
REQUEST_TIMEOUT | 300 | Timeout in seconds |
Performance
With optimized settings:
- Each iteration: 60-90 seconds
- Typical convergence: 2-3 iterations
- Total time: 2-4 minutes (distributed across multiple calls)
Using Haiku for critiques reduces iteration time by ~50%.
AI-Friendly Features
This tool includes special features for AI assistants using it:
- Auto Session Tracking: The
current_session_id
is automatically maintained - Smart Error Messages: Errors include
_ai_
prefixed fields with actionable diagnostics - Performance Hints: Responses include optimization tips and predictions
- Progress Predictions: Convergence tracking includes estimates of remaining iterations
Example AI-helpful error response:
{
"success": false,
"error": "No session_id provided and no current session",
"_ai_context": {
"current_session_id": null,
"active_session_count": 2,
"recent_sessions": [...]
},
"_ai_suggestion": "Use start_refinement to create a new session",
"_human_action": "Start a new refinement session first"
}
Architecture
┌─────────────┐ ┌──────────────┐ ┌─────────────┐
│ Claude │────▶│ MCP Server │────▶│ Bedrock │
│ Desktop │◀────│ │◀────│ Claude │
└─────────────┘ └──────────────┘ └─────────────┘
│
▼
┌──────────────┐
│ Session │
│ Manager │
└──────────────┘
Development
Running tests
uv run pytest tests/
Local testing
uv run python test_incremental.py
Attribution
This project is inspired by recursive-companion by Hank Besser. The original implementation provided the conceptual Draft → Critique → Revise → Converge pattern. This MCP version adds:
- Session-based incremental processing to avoid timeouts
- AWS Bedrock integration for Claude and Titan embeddings
- Domain auto-detection and specialized prompts
- Mathematical convergence measurement
- Support for different models for critiques vs generation
Contributing
Contributions are welcome! Please read our Contributing Guide for details.
License
MIT License - see LICENSE file for details.
Acknowledgments
- Original concept: Hank Besser's recursive-companion
- Built for the Model Context Protocol
- Uses AWS Bedrock for LLM access
- Inspired by iterative refinement patterns in AI reasoning