Sequential Thinking Multi-Agent System (MAS)

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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.

What This Is

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.

How It Works

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]

The Specialist Agents

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.

Agent	Thinking direction	Focus	Time budget
Factual	factual	Objective facts and verified data	120s
Emotional	emotional	Intuition and gut reactions	30s
Critical	critical	Risks, weaknesses, logical flaws	120s
Optimistic	optimistic	Benefits, opportunities, value	120s
Creative	creative	New ideas and alternatives	240s
Meta-cognitive	metacognitive	Bias detection and reasoning-process evaluation	90s
Synthesis	synthesis	Integration and final answer	60s

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.

Model Strategy

Two models are configured per provider:

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

Research Capabilities

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 sequentialthinking Tool

The server exposes one MCP tool.

Input

{
  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
}

Output

{
  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>
}

Call Contract

• 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.

Supported Providers

Provider	Env var	Default enhanced model	Default standard model
DeepSeek (default)	DEEPSEEK_API_KEY	deepseek-chat	deepseek-chat
Groq	GROQ_API_KEY	openai/gpt-oss-120b	openai/gpt-oss-20b
OpenRouter	OPENROUTER_API_KEY	deepseek/deepseek-chat-v3-0324	deepseek/deepseek-r1
GitHub Models	GITHUB_TOKEN	openai/gpt-5	openai/gpt-5-min
Anthropic	ANTHROPIC_API_KEY	claude-3-5-sonnet-20241022	claude-3-5-haiku-20241022
Ollama	none	devstral:24b	devstral:24b

Installation

Prerequisites

• 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

Install

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

uv pip install .        # or: pip install .

Configure an MCP Client

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"
      }
    }
  }
}

Environment Variables

# 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"

Run the Server Directly

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

Development

# 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

Test with MCP Inspector

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

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

Token Consumption Warning

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.

Project Structure

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

Changelog

See CHANGELOG.md for version history.

Contributing

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

License

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

Acknowledgments

• 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

Support

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