mcp-server-mas-sequential-thinking

# Sequential Thinking Multi-Agent System (MAS)  [](https://smithery.ai/server/@FradSer/mcp-server-mas-sequential-thinking) [](https://twitter.com/FradSer) [](https://www.python.org/downloads/) [](https://github.com/cognitivecomputations/agno) English | [简体中文](README.zh-CN.md) This project implements an advanced sequential thinking process using a **Multi-Agent System (MAS)** built with the **Agno** framework and served via **MCP**. It represents a significant evolution from simpler state-tracking approaches by leveraging coordinated, specialized agents for deeper analysis and problem decomposition. [](https://mseep.ai/app/fradser-mcp-server-mas-sequential-thinking) ## What is This? This is an **MCP server** - not a standalone application. It runs as a background service that extends your LLM client (like Claude Desktop) with sophisticated sequential thinking capabilities. The server provides a `sequentialthinking` tool that processes thoughts through multiple specialized AI agents, each examining the problem from a different cognitive angle. ## Core Architecture: Multi-Dimensional Thinking Agents The system employs **6 specialized thinking agents**, each focused on a distinct cognitive perspective: ### 1. **Factual Agent** - **Focus**: Objective facts and verified data - **Approach**: Analytical, evidence-based reasoning - **Capabilities**: - Web research for current facts (via ExaTools) - Data verification and source citation - Information gap identification - **Time allocation**: 120 seconds for thorough analysis ### 2. **Emotional Agent** - **Focus**: Intuition and emotional intelligence - **Approach**: Gut reactions and feelings - **Capabilities**: - Quick intuitive responses (30-second snapshots) - Visceral reactions without justification - Emotional pattern recognition - **Time allocation**: 30 seconds (quick reaction mode) ### 3. **Critical Agent** - **Focus**: Risk assessment and problem identification - **Approach**: Logical scrutiny and devil's advocate - **Capabilities**: - Research counterexamples and failures (via ExaTools) - Identify logical flaws and risks - Challenge assumptions constructively - **Time allocation**: 120 seconds for deep analysis ### 4. **Optimistic Agent** - **Focus**: Benefits, opportunities, and value - **Approach**: Positive exploration with realistic grounding - **Capabilities**: - Research success stories (via ExaTools) - Identify feasible opportunities - Explore best-case scenarios logically - **Time allocation**: 120 seconds for balanced optimism ### 5. **Creative Agent** - **Focus**: Innovation and alternative solutions - **Approach**: Lateral thinking and idea generation - **Capabilities**: - Cross-industry innovation research (via ExaTools) - Divergent thinking techniques - Multiple solution generation - **Time allocation**: 240 seconds (creativity needs time) ### 6. **Synthesis Agent** - **Focus**: Integration and metacognitive orchestration - **Approach**: Holistic synthesis and final answer generation - **Capabilities**: - Integrate all perspectives into coherent response - Answer the original question directly - Provide actionable, user-friendly insights - **Time allocation**: 60 seconds for synthesis - **Note**: Uses enhanced model, does NOT include ExaTools (focuses on integration) ## AI-Powered Intelligent Routing The system uses **AI-driven complexity analysis** to determine the optimal thinking sequence: ### Processing Strategies: 1. **Single Agent** (Simple questions) - Direct factual or emotional response - Fastest processing for straightforward queries 2. **Double Agent** (Moderate complexity) - Two-step sequences (e.g., Optimistic → Critical) - Balanced perspectives for evaluation tasks 3. **Triple Agent** (Core thinking) - Factual → Creative → Synthesis - Philosophical and analytical problems 4. **Full Sequence** (Complex problems) - All 6 agents orchestrated together - Comprehensive multi-perspective analysis The AI analyzer evaluates: - Problem complexity and semantic depth - Primary problem type (factual, emotional, creative, philosophical, etc.) - Required thinking modes for optimal solution - Appropriate model selection (Enhanced vs Standard) ### AI Routing Flow Diagram ```mermaid flowchart TD A[Input Thought] --> B[AI Complexity Analyzer] B --> C{Problem Analysis} C --> C1[Complexity Score<br/>0-100] C --> C2[Problem Type<br/>FACTUAL/EMOTIONAL/<br/>CREATIVE/PHILOSOPHICAL] C -