NSAF MCP Server

@ariunbolor

About NSAF MCP Server

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

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

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Overview

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

FAQ from NSAF MCP Server

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.

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.

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.

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.

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.

Frequently asked questions

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.

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`.

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.

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.

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.

Comments

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