FradSer_mcp-server-mas-sequential-thinking

Sequential Thinking Multi-Agent System (MAS)

This project implements a sophisticated sequential thinking process using a Multi-Agent System (MAS) built with the Agno framework and served via MCP. Key features include:

• Utilization of a Coordinating Agent to manage workflows.
• Specialized Agents handle specific sub-tasks such as planning, researching, analyzing, critiquing, and synthesizing.
• Active processing, analysis, and synthesis of thoughts by the agent team.
• Integration with external tools for dynamic information gathering.
• Robust data validation and logging system.
• Designed for complex problem-solving requiring a nuanced thinking process.

Key Differences from Original Version

The current Python/Agno implementation is a shift from the original TypeScript version, offering a multi-agent system architecture, enhanced intelligence, active processing, and robust validation, among others.

How it Works (Coordinate Mode)

The system operates in phases, including initiation, validation, coordination, delegation to specialist agents, response collection, synthesis, and return value. Each step is geared towards iterative thought processing, analysis, and revision.

Token Consumption Warning

The multi-agent system architecture results in high token usage. Users should budget accordingly as this system prioritizes depth and complexity over efficiency.

Prerequisites

Requires Python 3.10+, compatible LLM API access, and installation of dependencies using either uv or pip. MCP Server configuration is necessary.

Installation & Setup

Clone the repository, set environment variables, and install dependencies.

Usage

Start the server script, which will respond to stdio requests. The sequentialthinking tool accepts specific parameters to process iterative thoughts.

Logging

Logs are written to a specified path and include detailed information on processes.