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NSAF MCP Server

@ariunbolor

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ariunbolor

NSAF MCP Server について

The Neuro-Symbolic Autonomy Framework integrates neural, symbolic, and autonomous learning methods into a single, continuously evolving AI agent-building system. This prototype demonstrates the SCMA component, which enables AI agents to self-design new AI agents using Generative

#agent#ai#neural-network#symbolic-computation#bndagents

NSAF MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "nsaf-mcp-server": {
      "command": "python",
      "args": [
        "unified_example.py"
      ]
    }
  }
}

NSAF MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is NSAF MCP Server?

NSAF MCP Server is the MCP‑protocol interface of the Neuro‑Symbolic Autonomy Framework (NSAF), a unified Python system that combines quantum computing, symbolic reasoning, neural networks, and foundation models into a single autonomous AI platform. It is designed for developers building autonomous AI assistants and agents that need task clustering, agent evolution, knowledge graphs, and multi‑step planning.

How to use NSAF MCP Server?

Install dependencies with pip install -r requirements.txt, then run unified_example.py for a full demo or start the MCP server with python -m core.mcp_interface. In code, create an NSAFMCPServer instance to expose tools like run_nsaf_evolution, analyze_nsaf_memory, project_nsaf_intent, cluster_nsaf_tasks, and get_nsaf_status.

Key features of NSAF MCP Server

  • Quantum‑enhanced task clustering and optimization
  • Self‑Constructing Meta‑Agents (SCMA) that evolve specialized agents
  • Hyper‑Symbolic Memory with RDF‑based knowledge graphs
  • Multi‑step planning via Recursive Intent Projection (RIP)
  • Multi‑provider foundation model integration (OpenAI, Anthropic, Google)
  • Distributed computing with Ray and enterprise‑grade security (JWT, AES‑256)

Use cases of NSAF MCP Server

  • Building an AI system for predictive maintenance with accuracy and latency goals
  • Creating self‑evolving agents for complex problem decomposition
  • Enabling AI assistants to perform quantum‑enhanced reasoning and planning
  • Deploying a secure, scalable autonomous decision‑making backend in production

よくある質問

1

What are the runtime requirements?

Python 3.8+, 8GB+ RAM recommended, GPU optional for large models. Key dependencies include Qiskit, PyTorch, Ray, FastAPI, and foundation model clients.

2

How do I configure foundation model access?

Set environment variables `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, and `GOOGLE_API_KEY`. Additional configuration (e.g., quantum backends, databases) goes in `config/config.yaml`.

3

Does the server support authentication and encryption?

Yes. Production deployment uses JWT tokens, API keys, AES‑256 encryption at rest, and HTTPS/WSS. Role‑based access control and audit logging are included.

4

Can I use it with Claude or other AI assistants?

Yes. The MCP interface provides tools that any MCP‑compatible assistant (e.g., Claude) can invoke for clustering, memory analysis, intent projection, and status checks.

5

What are the performance characteristics?

Task clustering processes 1000+ tasks/sec, agent evolution handles 100 agents per generation, memory graphs support 1M+ nodes, and API responses are under 100ms.

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