NSAF MCP Server

NSAF MCP Server is an MCP server that connects AI assistants like Claude to the Neuro-Symbolic Autonomy Framework (NSAF). NSAF is a Python framework that unifies quantum computing, symbolic reasoning, neural networks, and foundation models into an autonomous AI system. Developed for researchers, AI engineers, and autonomous systems developers, this server exposes framework operations through the Model Context Protocol. By bridging NSAF to an MCP client, users can trigger task clustering, agent evolution, semantic knowledge graph lookups, and multi-step intent planning directly from their conversational interface. It facilitates working with complex autonomy workflows without needing to script manual pipeline executions. The underlying architecture leverages technologies such as Qiskit, PyTorch, Ray, and RDFlib to process complex goals and constraints, allowing AI agents to orchestrate self-constructing meta-agents, manage hyper-symbolic memory graphs, and monitor system execution status directly within standard assistant interfaces.

Category: AI & LLM Tooling

Tags: agent-framework, autonomy, framework, neuro-symbolic

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How to install and configure NSAF MCP Server

Follow these steps to set up and run NSAF MCP Server with your MCP client: 1. Ensure you have Python 3.8+ installed. 2. Clone the repository: bash git clone https://github.com/ariunbolor/nsaf-mcp-server.git cd nsaf-mcp-server 3. Install all required dependencies: bash pip install -r requirements.txt 4. Set optional environment variables for foundation models or databases: bash export OPENAI_API_KEY="your-openai-key" export ANTHROPIC_API_KEY="your-anthropic-key" 5. Run the MCP server module directly: bash python -m core.mcp_interface 6. To connect with an MCP client such as Claude Desktop, add an entry to your MCP configuration file pointing to python with the argument -m core.mcp_interface inside the cloned directory path, as documented in the repository.

What you can do with NSAF MCP Server

• Clustering complex tasks: Group and decompose multidimensional engineering problems into solvable subsets using quantum-enhanced algorithms. - Evolving autonomous meta-agents: Trigger the Self-Constructing Meta-Agents module to dynamically generate specialized agents tailored to custom operational requirements. - Querying hyper-symbolic memory: Search and analyze RDF-based knowledge graphs to retrieve semantic relationships across past problem-solving sessions. - Projecting multi-step intent: Run the Recursive Intent Projection module to calculate and optimize autonomous multi-stage planning paths for AI agents. - Monitoring autonomy framework status: Inspect active clusters, agent health, memory node counts, and pipeline execution metrics through client tool calls.

Key facts

• https://github.com/ariunbolor/nsaf-mcp-server
• AI & LLM Tooling
• agent-framework, autonomy, framework, neuro-symbolic

Part of MCP Servers

What is NSAF MCP Server?

NSAF MCP Server is an implementation of the Model Context Protocol for the Neuro-Symbolic Autonomy Framework. It lets AI assistants connect directly to NSAF core modules, including quantum-symbolic clustering, meta-agent evolution, and hyper-symbolic memory.

What tools are available in NSAF MCP Server?

The server provides tools such as run_nsaf_evolution for evolving specialized agents, analyze_nsaf_memory for querying semantic graphs, project_nsaf_intent for recursive planning, cluster_nsaf_tasks for quantum-enhanced decomposition, and get_nsaf_status for inspecting runtime health.

What license does NSAF MCP Server use?

NSAF MCP Server is released under the open source MIT License, as indicated in the project repository.

Which MCP clients work with NSAF MCP Server?

It is designed to work with any client supporting the Model Context Protocol, including Claude Desktop, OtterMind, and other custom developer environments executing python-based MCP server configurations.

What are the system prerequisites to run this server?

Running the server requires Python 3.8 or higher and at least 8GB of RAM. A GPU is optional for handling larger foundation models, and dependencies include Qiskit, PyTorch, Ray, and RDFlib.