Summary

This project provides an advanced sequential thinking process for LLM clients, leveraging a Multi-Agent System (MAS) built with the Agno framework and served via MCP. It enables deeper analysis and problem decomposition by orchestrating specialized AI agents, benefiting users who require sophisticated reasoning beyond simple state tracking.

How It Works

The core architecture employs 6 specialized thinking agents (Factual, Emotional, Critical, Optimistic, Creative, Synthesis), each with distinct cognitive roles and time allocations. An AI-driven complexity analyzer determines the optimal processing strategy—ranging from single-agent responses to a full sequence involving all agents—and routes thoughts accordingly. Non-synthesis agents execute in parallel for efficiency, with a dedicated Synthesis agent integrating perspectives for a coherent, actionable output. This MAS approach facilitates coordinated, multi-dimensional analysis.

Quick Start & Requirements

Installation is recommended via npx -y @smithery/cli install @FradSer/mcp-server-mas-sequential-thinking --client claude. Manual installation involves cloning the repository and running uv pip install . or pip install .. Prerequisites include Python 3.10+, an LLM API key (DeepSeek, Groq, OpenRouter, GitHub, Anthropic, or Ollama), and optionally an EXA_API_KEY for web research. The uv package manager is recommended. Configuration involves setting environment variables for LLM provider and API keys within an MCP client.

Highlighted Details

• Multi-Dimensional Agents: Six specialized agents (Factual, Emotional, Critical, Optimistic, Creative, Synthesis) offer distinct cognitive perspectives.
• AI-Powered Routing: Dynamically selects processing strategies (Single, Double, Triple, Full Sequence) based on problem complexity and type.
• Integrated Research: Four agents can perform web research via ExaTools (optional, requires EXA_API_KEY).
• Dual Model Strategy: Utilizes enhanced models for synthesis and standard models for individual agent tasks.
• Provider Agnostic: Supports multiple LLM providers including DeepSeek (default), Groq, OpenRouter, Anthropic, and local Ollama.

Maintenance & Community

No specific details on contributors, sponsorships, or community channels (like Discord/Slack) are provided in the README. GitHub Issues are the designated channel for bug reports and feature requests.

Licensing & Compatibility

The project is licensed under the MIT License, which is permissive for commercial use and integration into closed-source projects.

Limitations & Caveats

The multi-agent, parallel processing architecture leads to significantly higher token consumption (potentially 5-10x) compared to simpler approaches. Complex reasoning sequences may require longer processing times. Web research capabilities incur additional costs via the Exa API. This project functions as an MCP server and requires an MCP-compatible client; it is not a standalone application.