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Iglesias","email":"nfiglesias@gmail.com"},"license":"MIT","homepage":"https://github.com/cyqlelabs/mcp-dual-cycle-reasoner#readme","keywords":["mcp","dual-cycle","metacognition","reasoning","autonomous-agents","cognitive-architecture","self-monitoring","loop-detection","belief-revision","case-based-reasoning"],"repository":{"type":"git","url":"git+https://github.com/cyqlelabs/mcp-dual-cycle-reasoner.git"},"description":"MCP server implementing dual-cycle metacognitive reasoning framework for autonomous agents","maintainers":[{"name":"danderian","email":"nfiglesias@gmail.com"}],"readme":"<h1 align=\"center\">MCP Dual-Cycle Reasoner</h1>\n<p align=\"center\"><img src=\"slag-brothers-hermanos-macana.gif\" /></p>\n\n[![CI](https://github.com/cyqlelabs/mcp-dual-cycle-reasoner/actions/workflows/ci.yml/badge.svg)](https://github.com/cyqlelabs/mcp-dual-cycle-reasoner/actions/workflows/ci.yml)\n[![codecov](https://codecov.io/gh/cyqlelabs/mcp-dual-cycle-reasoner/branch/main/graph/badge.svg)](https://codecov.io/gh/cyqlelabs/mcp-dual-cycle-reasoner)\n\nA 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.\n\n<a href=\"https://glama.ai/mcp/servers/@cyqlelabs/mcp-dual-cycle-reasoner\">\n  <img width=\"380\" height=\"200\" src=\"https://glama.ai/mcp/servers/@cyqlelabs/mcp-dual-cycle-reasoner/badge\" alt=\"Dual-Cycle Reasoner MCP server\" />\n</a>\n\n## Description\n\nThe 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.\n\nThe framework consists of two main components:\n\n- **Sentinel**: Monitors the agent's actions and detects anomalies, such as action repetition, state invariance, and progress stagnation.\n- **Adjudicator**: Manages a case base of past experiences, allowing the agent to store and retrieve solutions to previously encountered problems.\n\nThis 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.\n\n## Key Features\n\n- 📊 **Advanced Statistical Analysis**: Entropy-based anomaly detection and time series analysis.\n- 🧠 **Enhanced Case-Based Reasoning**: Semantic similarity matching with NLI-based text analysis.\n- 🎯 **Multi-Strategy Detection**: Statistical, pattern-based, and hybrid loop detection.\n- 📈 **Time Series Analysis**: Trend detection and cyclical pattern recognition.\n- 🔧 **Configurable Detection**: Domain-specific thresholds and progress indicators.\n- 🎨 **Intelligent Case Management**: Quality scoring, deduplication, and usage-based optimization.\n- 🚀 **High-Performance Libraries**: Built with `simple-statistics`, `natural`, `compromise`, and HuggingFace Transformers.\n\n## Tech Stack\n\n- **Language**: TypeScript\n- **Framework**: Node.js\n- **Server**: FastMCP for SSE transport\n- **NLP and Machine Learning**:\n  - `@huggingface/transformers`: For NLI-based semantic analysis\n  - `natural`: For sentiment analysis and tokenization\n  - `compromise`: For natural language processing\n- **Statistics**:\n  - `simple-statistics`: For statistical calculations\n  - `ml-matrix`: For matrix operations\n- **Development Tools**:\n  - `jest`: For testing\n  - `eslint`: For linting\n  - `prettier`: For code formatting\n  - `zod`: For schema validation\n\n## Installation\n\nTo get the project running locally, follow these steps:\n\n1.  **Clone the repository**:\n\n    ```bash\n    git clone https://github.com/cyqlelabs/mcp-dual-cycle-reasoner.git\n    cd mcp-dual-cycle-reasoner\n    ```\n\n2.  **Install dependencies**:\n\n    ```bash\n    npm install\n    ```\n\n3.  **Build the project**:\n    ```bash\n    npm run build\n    ```\n\n## Usage\n\n### Running the Server\n\nYou can run the server in two modes:\n\n1.  **HTTP Stream (Default)**:\n\n    ```bash\n    npm start\n    ```\n\n    The server will start on port 8080.\n\n2.  **Stdio**:\n    ```bash\n    npm start -- --stdio\n    ```\n\n### Using with Claude Desktop\n\nAdd the following to your Claude Desktop MCP configuration:\n\n```json\n{\n  \"mcpServers\": {\n    \"dual-cycle-reasoner\": {\n      \"command\": \"npx\",\n      \"args\": [\"@cyqlelabs/mcp-dual-cycle-reasoner\"]\n    }\n  }\n}\n```\n\nFor stdio transport, add the `--stdio` flag to the `args` array.\n\n## Available Tools\n\n### Core Monitoring Tools\n\n#### `start_monitoring`\n\nInitialize metacognitive monitoring of an agent's cognitive process.\n\n**Input Schema**:\n\n```typescript\n{\n  goal: string; // Current goal being pursued\n  initial_beliefs?: string[]; // Initial beliefs about the task\n}\n```\n\n#### `process_trace_update`\n\nMain monitoring function—processes cognitive trace updates from the agent.\n\n**Input Schema**:\n\n```typescript\n{\n  last_action: string; // Latest action name\n  current_context?: string; // Current environment context\n  goal: string; // Current goal being pursued\n  window_size?: number; // Monitoring window size (default: 10)\n}\n```\n\n**Return Payload**:\n\nReturns a JSON object indicating if intervention is required and details about any detected loop.\n\n```json\n{\n  \"intervention_required\": true,\n  \"loop_detected\": {\n    \"detected\": true,\n    \"type\": \"action_repetition\",\n    \"confidence\": 0.85,\n    \"details\": \"Loop detected via parameter_repetition: 57% anomaly score...\",\n    \"actions_involved\": [\"click_submit_button\"]\n  }\n}\n```\n\n#### `stop_monitoring`\n\nStop metacognitive monitoring and get a session summary.\n\n**Input Schema**: `{}`\n\n### Loop Detection Tools\n\n#### `detect_loop`\n\nDetect if the agent is stuck in a loop using various strategies.\n\n**Input Schema**:\n\n```typescript\n{\n  current_context?: string; // Current environment context\n  goal: string; // Current goal being pursued\n  detection_method?: \"statistical\" | \"pattern\" | \"hybrid\"; // Detection method (default: \"hybrid\")\n}\n```\n\n**Return Payload**:\n\nReturns a `LoopDetectionResult` object as a JSON string.\n\n```json\n{\n  \"detected\": true,\n  \"type\": \"action_repetition\",\n  \"confidence\": 0.85,\n  \"details\": {\n    \"dominant_method\": \"parameter_repetition\",\n    \"anomaly_score\": 0.57,\n    \"actions_involved_count\": 1,\n    \"recent_actions_count\": 10,\n    \"metrics\": {\n      \"semantic_repetition\": 0.6,\n      \"parameter_repetition\": 0.8\n    }\n  },\n  \"actions_involved\": [\"click_submit_button\"]\n}\n```\n\n#### `configure_detection`\n\nConfigure loop detection parameters and domain-specific progress indicators.\n\n**Input Schema**:\n\n```typescript\n{\n  progress_indicators?: string[]; // default: []\n  min_actions_for_detection?: number; // default: 5\n  alternating_threshold?: number; // default: 0.5\n  repetition_threshold?: number; // default: 0.4\n  progress_threshold_adjustment?: number; // default: 0.2\n  semantic_intents?: string[]; // default: []\n}\n```\n\n### Enhanced Experience Management\n\n#### `store_experience`\n\nStore a case for future case-based reasoning with enhanced metadata and quality scoring.\n\n**Input Schema**:\n\n```typescript\n{\n  problem_description: string;\n  solution: string;\n  outcome: boolean;\n  context?: string;\n  difficulty_level?: \"low\" | \"medium\" | \"high\";\n}\n```\n\n#### `retrieve_similar_cases`\n\nRetrieve similar cases using advanced semantic matching and filtering.\n\n**Input Schema**:\n\n```typescript\n{\n  problem_description: string;\n  max_results?: number; // default: 5\n  context_filter?: string;\n  difficulty_filter?: \"low\" | \"medium\" | \"high\";\n  outcome_filter?: boolean;\n  min_similarity?: number; // default: 0.1\n}\n```\n\n**Return Payload**:\n\nReturns an array of `Case` objects as a JSON string.\n\n```json\n[\n  {\n    \"id\": \"case-123\",\n    \"problem_description\": \"Form submission button not responding to clicks\",\n    \"solution\": \"Ensure all required fields are filled correctly.\",\n    \"outcome\": true,\n    \"context\": \"registration_form\",\n    \"difficulty_level\": \"medium\",\n    \"similarity_metrics\": {\n      \"combined_similarity\": 0.92\n    }\n  }\n]\n```\n\n### System Tools\n\n#### `get_monitoring_status`\n\nGet the current monitoring status and statistics.\n\n**Input Schema**: `{}`\n\n**Return Payload**:\n\nReturns a JSON string with the current monitoring status, including `is_monitoring`, `current_goal`, `trace_length`, and `intervention_count`.\n\n```json\n{\n  \"is_monitoring\": true,\n  \"current_goal\": \"Complete user registration process on website\",\n  \"trace_length\": 15,\n  \"intervention_count\": 2,\n  \"recent_actions\": [\n    { \"type\": \"click_submit_button\", \"timestamp\": 1678886400000 },\n    { \"type\": \"click_submit_button\", \"timestamp\": 1678886401000 }\n  ]\n}\n```\n\n#### `reset_engine`\n\nReset the dual-cycle engine state.\n\n**Input Schema**: `{}`\n\n## Example Usage Scenario\n\n## Example Usage Scenario\n\nHere's a complete example showing how to use the dual-cycle reasoner to monitor an autonomous agent and build up experience over time:\n\n### 1. Initial Setup and Configuration\n\n```typescript\n// Configure detection parameters for your domain\nawait configure_detection({\n  progress_indicators: ['page_loaded', 'form_submitted', 'data_extracted'],\n  min_actions_for_detection: 3,\n  alternating_threshold: 0.6,\n  repetition_threshold: 0.3,\n  semantic_intents: [\n    'navigating to page',\n    'clicking element',\n    'filling form field',\n    'submitting form',\n    'validating input',\n    'handling popup',\n    'extracting data',\n    'waiting for response',\n  ],\n});\n\n// Start monitoring the agent's goal\nawait start_monitoring({\n  goal: 'Complete user registration process on website',\n});\n```\n\n### 2. Monitoring Agent Actions\n\n```typescript\n// Monitor each action the agent takes\nawait process_trace_update({\n  last_action: 'click_signup_button',\n  current_context: 'homepage',\n  goal: 'Complete user registration process on website',\n});\n\n// ... agent continues actions ...\n\n// Agent gets stuck clicking submit repeatedly\nawait process_trace_update({\n  last_action: 'click_submit_button',\n  current_context: 'registration_form',\n  goal: 'Complete user registration process on website',\n});\n// Returns: {\n//   \"intervention_required\": true,\n//   \"loop_detected\": {\n//     \"detected\": true,\n//     \"type\": \"action_repetition\",\n//     \"confidence\": 0.85,\n//     \"details\": {\n//       \"dominant_method\": \"parameter_repetition\",\n//       \"anomaly_score\": 0.57,\n//       \"actions_involved_count\": 1,\n//       \"recent_actions_count\": 10,\n//       \"metrics\": {\n//         \"semantic_repetition\": 0.6,\n//         \"parameter_repetition\": 0.8\n//       }\n//     },\n//     \"actions_involved\": [\"click_submit_button\"]\n//   }\n// }\n```\n\n### 3. Storing and Retrieving Experiences\n\n```typescript\n// Store a successful experience\nawait store_experience({\n  problem_description: 'Email validation error blocking form submission',\n  solution: 'Check email format and retry with valid email address',\n  outcome: true,\n  context: 'registration_form',\n  difficulty_level: 'medium',\n});\n\n// When a loop is detected, retrieve similar cases for recovery\nconst similarCases = await retrieve_similar_cases({\n  problem_description: 'Form submission button not responding to clicks',\n  max_results: 3,\n  context_filter: 'registration_form',\n  outcome_filter: true,\n});\n```\n\n## Understanding Loop Detection\n\nWhen the `process_trace_update` tool detects a potential loop, it returns a detailed `loop_detected` object. Understanding the components of this object can help you diagnose and debug agent behavior.\n\nHere's a quick guide to the key metrics in the `details` object:\n\n- **`dominant_method`**: The primary method that triggered the loop detection (e.g., `semantic_repetition`, `state_invariance`).\n- **`anomaly_score`**: An overall score (0-1) representing the confidence that the agent's recent actions are part of a loop. It's a weighted average of multiple detection methods.\n- **`actions_involved_count`**: The number of recent actions identified as being part of the detected loop.\n- **`recent_actions_count`**: The total number of recent actions analyzed.\n- **`metrics`**: An object containing the raw scores from the different detection methods, such as:\n  - **`semantic_repetition`**: The percentage of recent actions that are semantically similar to each other.\n  - **`parameter_repetition`**: The percentage of similarity between the parameters of semantically similar actions.\n  - **`exact_repetition`**: The percentage of recent actions that are exact, character-for-character duplicates.\n  - **`cyclical_pattern`**: The score (0-1) indicating the presence of repeating sequences of actions.\n  - **`oscillation_pattern`**: The score (0-1) indicating the presence of oscillating (back-and-forth) actions.\n  - **`alternating_pattern`**: The score (0-1) indicating the presence of alternating action patterns.\n\n## Contributing\n\nContributions are welcome! Please read the contributing guidelines and ensure all tests pass before submitting a pull request.\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n","readmeFilename":"README.md"}