Sequential Thinking Multi-Agent MCP MCP Server

About

## Sequential Thinking MAS MCP Server: Multi-Agent Reasoning The **Sequential Thinking MAS MCP Server** extends sequential thinking with multi-agent systems for Google Antigravity. This integration coordinates multiple AI agents for complex problem-solving, with each agent specializing in different aspects of the solution. ### Why Sequential Thinking MAS MCP? Multi-agent systems excel at complexity: - **Specialized Agents**: Expert agents for each domain - **Collaboration**: Agents work together on problems - **Parallel Processing**: Concurrent reasoning paths - **Debate & Consensus**: Agents challenge each other - **Better Solutions**: Diverse perspectives improve output ### Key Features #### 1. Agent Definition ```python from sequential_thinking_mas import MultiAgentSystem mas = MultiAgentSystem() # Define specialized agents mas.add_agent( name="architect", role="System design and architecture decisions", expertise=["microservices", "scalability", "patterns"] ) mas.add_agent( name="security", role="Security analysis and recommendations", expertise=["authentication", "encryption", "compliance"] ) mas.add_agent( name="performance", role="Performance optimization", expertise=["caching", "databases", "profiling"] ) ``` #### 2. Collaborative Solving ```python # Agents collaborate on problem result = mas.solve( problem="Design a secure, scalable payment processing system", coordination="sequential" # or "parallel", "debate" ) for agent_response in result.contributions: print(f"Agent: {agent_response.agent}") print(f"Contribution: {agent_response.content}") ``` #### 3. Agent Debate ```python # Agents debate approaches debate = mas.debate( topic="Should we use GraphQL or REST for the API?", rounds=3 ) print(f"Conclusion: {debate.consensus}") print(f"Key arguments: {debate.summary}") ``` ### Configuration ```json { "mcpServers": { "sequential-thinking-mas": { "command": "npx", "args": ["-y", "@anthropic/mcp-sequential-mas"], "env": { "MAS_MODEL": "gpt-4-turbo", "MAX_AGENTS": "5", "DEBATE_ROUNDS": "3" } } } } ``` ### Use Cases **Architecture Review**: Multiple agents review from different angles. **Complex Decisions**: Debate trade-offs with specialized perspectives. **Comprehensive Analysis**: Parallel analysis across domains. The Sequential Thinking MAS MCP Server brings multi-agent reasoning to Antigravity.

Installation

{
  "mcpServers": {
    "sequential-thinking-mas": {
      "mcpServers": {
        "mas-thinking": {
          "env": {
            "GROQ_API_KEY": "YOUR_KEY"
          },
          "args": [
            "-y",
            "mas-sequential-thinking-mcp"
          ],
          "command": "npx"
        }
      }
    }
  }
}

How to Use

1. 1Choose LLM provider
2. 2Ollama for local inference
3. 3Complex reasoning tasks