mcp-server-mas-sequential-thinking
An advanced sequential thinking process using a Multi-Agent System (MAS) built with the Agno framework and served via MCP.
Documentation
Sequential Thinking Multi-Agent System (MAS) !
English | 简体中文
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
}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 uvDevelopment
# 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 checksTest with MCP Inspector
npx @modelcontextprotocol/inspector uv run mcp-server-mas-sequential-thinkingOpen 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
└── MakefileChangelog
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
Frequently asked questions
What is mcp-server-mas-sequential-thinking?
mcp-server-mas-sequential-thinking is An advanced sequential thinking process using a Multi-Agent System (MAS) built with the Agno framework and served via MCP.
How do I install mcp-server-mas-sequential-thinking?
Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.
Is mcp-server-mas-sequential-thinking open source?
Yes — it is hosted on GitHub at https://github.com/FradSer/mcp-server-mas-sequential-thinking and has 265 stars.
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