MCP Dual-Cycle Reasoner
A Model Context Protocol (MCP) server implementing the Dual-Cycle Metacognitive Reasoning Framework for autonomous agents. This tool empowers agents with greater self-awareness and reliability through intelligent loop detection and experience acquisition.
Description
The MCP Dual-Cycle Reasoner is a sophisticated tool designed to enhance the autonomy and reliability of AI agents. By implementing a dual-cycle metacognitive framework, it provides agents with the ability to monitor their own cognitive processes, detect when they are stuck in repetitive loops, and learn from past experiences to make better decisions.
The framework consists of two main components:
- Sentinel: Monitors the agent's actions and detects anomalies, such as action repetition, state invariance, and progress stagnation.
- Adjudicator: Manages a case base of past experiences, allowing the agent to store and retrieve solutions to previously encountered problems.
This server is built with TypeScript and leverages high-performance libraries for statistical analysis, natural language processing, and semantic similarity, enabling advanced features like entropy-based anomaly detection, NLI-based text analysis, and intelligent case management.
Key Features
- 📊 Advanced Statistical Analysis: Entropy-based anomaly detection and time series analysis.
- 🧠 Enhanced Case-Based Reasoning: Semantic similarity matching with NLI-based text analysis.
- 🎯 Multi-Strategy Detection: Statistical, pattern-based, and hybrid loop detection.
- 📈 Time Series Analysis: Trend detection and cyclical pattern recognition.
- 🔧 Configurable Detection: Domain-specific thresholds and progress indicators.
- 🎨 Intelligent Case Management: Quality scoring, deduplication, and usage-based optimization.
- 🚀 High-Performance Libraries: Built with
simple-statistics, natural, compromise, and HuggingFace Transformers.
Tech Stack
- Language: TypeScript
- Framework: Node.js
- Server: FastMCP for SSE transport
- NLP and Machine Learning:
@huggingface/transformers: For NLI-based semantic analysisnatural: For sentiment analysis and tokenizationcompromise: For natural language processing
- Statistics:
simple-statistics: For statistical calculationsml-matrix: For matrix operations
- Development Tools:
jest: For testingeslint: For lintingprettier: For code formattingzod: For schema validation
Installation
To get the project running locally, follow these steps:
- Clone the repository:
git clone https://github.com/cyqlelabs/mcp-dual-cycle-reasoner.git
cd mcp-dual-cycle-reasoner
- Install dependencies:
- Build the project:
Usage
Running the Server
You can run the server in two modes:
- HTTP Stream (Default):The server will start on port 8080.
- Stdio:
Using with Claude Desktop
Add the following to your Claude Desktop MCP configuration:
{
"mcpServers": {
"dual-cycle-reasoner": {
"command": "npx",
"args": ["@cyqlelabs/mcp-dual-cycle-reasoner"]
}
}
}
For stdio transport, add the --stdio flag to the args array.
start_monitoring
Initialize metacognitive monitoring of an agent's cognitive process.
Input Schema:
{
goal: string; // Current goal being pursued
initial_beliefs?: string[]; // Initial beliefs about the task
}
process_trace_update
Main monitoring function—processes cognitive trace updates from the agent.
Input Schema:
{
last_action: string; // Latest action name
current_context?: string; // Current environment context
goal: string; // Current goal being pursued
window_size?: number; // Monitoring window size (default: 10)
}
stop_monitoring
Stop metacognitive monitoring and get a session summary.
Input Schema: {}
detect_loop
Detect if the agent is stuck in a loop using various strategies.
Input Schema:
{
current_context?: string; // Current environment context
goal: string; // Current goal being pursued
detection_method?: "statistical" | "pattern" | "hybrid"; // Detection method (default: "hybrid")
}
Configure loop detection parameters and domain-specific progress indicators.
Input Schema:
{
progress_indicators?: string[];
min_actions_for_detection?: number;
alternating_threshold?: number;
repetition_threshold?: number;
progress_threshold_adjustment?: number;
semantic_intents?: string[];
}
Enhanced Experience Management
store_experience
Store a case for future case-based reasoning with enhanced metadata and quality scoring.
Input Schema:
{
problem_description: string;
solution: string;
outcome: boolean;
context?: string;
goal_type?: string;
difficulty_level?: "low" | "medium" | "high";
}
retrieve_similar_cases
Retrieve similar cases using advanced semantic matching and filtering.
Input Schema:
{
problem_description: string;
max_results?: number;
context_filter?: string;
goal_type_filter?: string;
difficulty_filter?: "low" | "medium" | "high";
outcome_filter?: boolean;
min_similarity?: number;
}
get_monitoring_status
Get the current monitoring status and statistics.
Input Schema: {}
reset_engine
Reset the dual-cycle engine state.
Input Schema: {}
Example Usage Scenario
Here's a complete example showing how to use the dual-cycle reasoner to monitor an autonomous agent and build up experience over time:
1. Initial Setup and Configuration
// Configure detection parameters for your domain
await configure_detection({
progress_indicators: ['page_loaded', 'form_submitted', 'data_extracted'],
min_actions_for_detection: 3,
alternating_threshold: 0.6,
repetition_threshold: 0.3,
semantic_intents: [
'navigating to page',
'clicking element',
'filling form field',
'submitting form',
'validating input',
'handling popup',
'extracting data',
'waiting for response',
],
});
// Start monitoring the agent's goal
await start_monitoring({
goal: 'Complete user registration process on website',
});
2. Monitoring Agent Actions
// Monitor each action the agent takes
await process_trace_update({
last_action: 'click_signup_button',
current_context: 'homepage',
goal: 'Complete user registration process on website',
});
// ... agent continues actions ...
// Agent gets stuck clicking submit repeatedly
await process_trace_update({
last_action: 'click_submit_button',
current_context: 'registration_form',
goal: 'Complete user registration process on website',
});
// Returns: {
// "intervention_required": true,
// "loop_detected": {
// "detected": true,
// "type": "action_repetition",
// "confidence": 0.85,
// "details": "Loop detected via parameter_repetition: 57% anomaly score...",
// "actions_involved": ["click_submit_button"]
// }
// }
3. Storing and Retrieving Experiences
// Store a successful experience
await store_experience({
problem_description: 'Email validation error blocking form submission',
solution: 'Check email format and retry with valid email address',
outcome: true,
context: 'registration_form',
goal_type: 'user_registration',
difficulty_level: 'medium',
});
// When a loop is detected, retrieve similar cases for recovery
const similarCases = await retrieve_similar_cases({
problem_description: 'Form submission button not responding to clicks',
max_results: 3,
context_filter: 'registration_form',
outcome_filter: true,
});
Contributing
Contributions are welcome! Please read the contributing guidelines and ensure all tests pass before submitting a pull request.
License
This project is licensed under the MIT License. See the LICENSE file for details.