by FradSer
An advanced sequential thinking process using a Multi-Agent System (MAS) built with the Agno framework and served via MCP.
# Add to your Claude Code skills
git clone https://github.com/FradSer/mcp-server-mas-sequential-thinkingEnglish | 简体中文
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.
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.
The system employs 6 specialized thinking agents, each focused on a distinct cognitive perspective:
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The system uses AI-driven complexity analysis to determine the optimal thinking sequence:
full_exploration is mandatory for all requestsThe AI analyzer still evaluates:
flowchart TD
A[Input Thought] --> B[AI Complexity Analyzer]
B --> C[Complexity Metadata Stored]
C --> D[Fixed Strategy: full_exploration]
D --> E[Step 1: Initial Synthesis]
E --> F[Step 2: Parallel Specialist Agents]
F --> G[Step 3: Final Synthesis]
G --> H[Unified Response]
Key Insights:
4 out of 6 agents are equipped with web research capabilities via ExaTools:
Research is optional - requires EXA_API_KEY environment variable. The system works perfectly without it, using pure reasoning capabilities.
This Python/Agno implementation marks a fundamental shift from the original TypeScript version:
| Feature/Aspect | Python/Agno Version (Current) | TypeScript Version (Original) |
| :------------------ | :------------------------------------------------------------------- | :--------------------------------------------------- |
| Architecture | Multi-Agent System (MAS); Active processing by a team of agents. | Single Class State Tracker; Simple logging/storing. |
| Intelligence | Distributed Agent Logic; Embedded in specialized agents & Coordinator. | External LLM Only; No internal intelligence. |
| Processing | Active Analysis & Synthesis; Agents act on the thought. | Passive Logging; Merely recorded the thought. |
| Frameworks | Agno (MAS) + FastMCP (Server); Uses dedicated MAS library. | MCP SDK only. |
| Coordination | Explicit Team Coordination Logic (Team in coordinate mode). | None; No coordination concept. |
| Validation | Pydantic Schema Validation; Robust data validation. | Basic Type Checks; Less reliable. |
| External Tools | Integrated (Exa via Researcher); Can perform research tasks. | None. |
| Logging | Structured Python Logging (File + Console); Configurable. | Console Logging with Chalk; Basic. |
| Language & Ecosystem | Python; Leverages Python AI/ML ecosystem. | TypeScript/Node.js. |
In essence, the system evolved from a passive thought recorder to an active thought processor powered by a collaborative team of AI agents.
sequentialthinking tool to define the problem and initiate the process.sequentialthinking tool with the current thought, structured according to the ThoughtData model.full_exploration multi-step sequence.High Token Usage: Due to the Multi-Agent System architecture, this tool consumes significantly more tokens than single-agent alternatives or the previous TypeScript version. Each sequentialthinking call invokes multiple specialized agents simultaneously, leading to substant