mcp-server-mas-sequential-thinking

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An MCP server that processes sequential thoughts through a team of specialized AI agents, each analyzing the problem from a different cognitive perspective.

This is an MCP server, not a standalone application. It runs as a background service that extends an MCP-compatible LLM client (like Claude Desktop) with structured sequential-thinking capabilities. It exposes one tool, sequentialthinking, that runs every thought through a fixed multi-agent workflow: an initial synthesis, several specialist agents thinking in parallel, and a final synthesis that answers the original question.

The system uses a fixed full_exploration strategy for every request. The AI complexity analyzer still runs to record diagnostic metadata (complexity score, problem type, required thinking modes), but it no longer changes the execution path — all thoughts take the same route:

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]

Each request runs six specialist agents in parallel, plus a synthesis agent that runs twice (once at the start, once at the end). Every specialist except synthesis can optionally use web research via ExaTools.

Key properties:

• Deterministic: every request runs the same multi-step path.
• Parallel: the specialist agents run simultaneously with asyncio.gather.
• Synthesis-driven: both orchestration and the final answer come from the synthesis agent, which uses the enhanced model.

Two models are configured per provider:

• Enhanced model: used by the synthesis agent (integration tasks).
• Standard model: used by the specialist agents.

ExaTools is attached to every agent except synthesis. Research is optional — it activates only when EXA_API_KEY is set. Without it, the system works on pure reasoning.

The server exposes one MCP tool.

{
  thought: string,               // One focused reasoning step
  thoughtNumber: number,         // 1-based step index; increment each call
  totalThoughts: number,         // Planned number of steps
  nextThoughtNeeded: boolean,    // true for intermediate steps, false on final step
  isRevision: boolean,           // true only when revising earlier conclusions
  branchFromThought?: number,    // Set with branchId to branch from a prior step
  branchId?: string,             // Branch identifier (required when branching)
  needsMoreThoughts: boolean     // true only when extending beyond totalThoughts
}

{
  should_continue: boolean,      // Canonical continuation signal
  next_thought_number: number?,  // Recommended next thoughtNumber
  stop_reason: string,           // Why to continue/stop/retry
  current_thought_number: number,
  total_thoughts: number,
  next_call_arguments?: {        // Suggested next-call arguments when applicable
    thoughtNumber: number,
    totalThoughts: number,
    nextThoughtNeeded: boolean,
    needsMoreThoughts: boolean
  },
  parameter_usage: Record<string, string>
}

• Treat this tool as a multi-step loop, not a one-shot call.
• After every response, read structuredContent.should_continue.
• Keep calling until should_continue is false.
• Actively use reflection: when a step is weak or incorrect, send a revision step with isRevision=true.
• Prefer structuredContent.next_thought_number and next_call_arguments when building the next request.

• Python 3.10+
• An LLM API key from one of the providers above
• Optional: EXA_API_KEY for web research
• uv package manager (recommended) or pip

git clone https://github.com/FradSer/mcp-server-mas-sequential-thinking.git
cd mcp-server-mas-sequential-thinking

uv pip install .        # or: pip install .

Add to your MCP client configuration:

{
  "mcpServers": {
    "sequential-thinking": {
      "command": "mcp-server-mas-sequential-thinking",
      "env": {
        "LLM_PROVIDER": "deepseek",
        "DEEPSEEK_API_KEY": "your_api_key",
        "EXA_API_KEY": "your_exa_key_optional"
      }
    }
  }
}

# LLM provider (required)
LLM_PROVIDER="deepseek"  # deepseek, groq, openrouter, github, anthropic, ollama
DEEPSEEK_API_KEY="sk-..."

# Optional: override the models per provider (prefixed by provider name)
# DEEPSEEK_ENHANCED_MODEL_ID="deepseek-chat"
# DEEPSEEK_STANDARD_MODEL_ID="deepseek-chat"

# Optional: web research (enables ExaTools)
# EXA_API_KEY="your_exa_api_key"

# Optional: custom endpoint
# LLM_BASE_URL="https://custom-endpoint.com"

# Optional: team orchestration mode (standard/broadcast, route, coordinate)
# TEAM_MODE="standard"

mcp-server-mas-sequential-thinking        # installed script
uv run mcp-server-mas-sequential-thinking  # or via uv

# Install with dev dependencies
uv pip install -e ".[dev]"

# Code quality
uv run ruff check . --fix
uv run ruff format .
uv run mypy .

# Run tests
uv run pytest tests/

# Or use the Makefile
make test        # all tests with coverage + quality checks
make test-fast   # fast run without coverage
make check-all   # all quality checks

npx @modelcontextprotocol/inspector uv run mcp-server-mas-sequential-thinking

Open http://127.0.0.1:6274/ and test the sequentialthinking tool.

The multi-agent architecture consumes significantly more tokens than a single-agent tool — roughly 5-10x more per sequentialthinking call, because every call invokes multiple specialist agents. The tradeoff is deeper, multi-perspective analysis.

mcp-server-mas-sequential-thinking/
├── src/mcp_server_mas_sequential_thinking/
│   ├── main.py                          # MCP server entry point (MCPServer)
│   ├── processors/
│   │   ├── multi_thinking_core.py       # Specialist agent definitions
│   │   └── multi_thinking_processor.py  # Parallel sequence execution
│   ├── routing/
│   │   ├── ai_complexity_analyzer.py    # AI complexity analysis
│   │   ├── complexity_types.py          # Complexity metric models
│   │   └── multi_thinking_router.py     # Fixed full_exploration routing
│   ├── services/
│   │   ├── server_core.py               # ThoughtProcessor implementation
│   │   ├── processing_orchestrator.py   # Agno Team orchestration
│   │   ├── workflow_executor.py
│   │   └── context_builder.py
│   ├── infrastructure/
│   │   ├── persistent_memory.py         # SQLite session storage
│   │   └── learning_resources.py        # Agent learning machine
│   ├── security/rate_limiter.py         # Rate limiting and request validation
│   └── config/
│       ├── modernized_config.py         # Provider strategies
│       └── constants.py                 # System constants
├── scripts/mcp_python_client_smoke.py   # Protocol smoke test
├── tests/                               # Unit and integration tests
├── pyproject.toml
└── Makefile

See CHANGELOG.md for version history.

Contributions are welcome. Please ensure:

1. Code follows the project style (ruff, mypy)
2. Commit messages use conventional commits format
3. All tests pass before submitting a PR
4. Documentation is updated as needed

This project does not yet declare a license. See the LICENSE discussion if you need to reuse it.

• Built with Agno v2.x
• Model Context Protocol by Anthropic
• Research capabilities powered by Exa (optional)
• Multi-dimensional thinking inspired by Edward de Bono's work

• GitHub Issues: Report bugs or request features
• Documentation: see CLAUDE.md for implementation notes
• MCP Protocol: Official MCP Documentation