Bridge AI Agents to Your Network Infrastructure with pyATS MCP

Stop building custom network automation APIs every time you want AI agents to interact with your Cisco infrastructure. The pyATS MCP Server gives you a production-ready bridge between any MCP-compatible AI system and your network devices through Cisco's battle-tested pyATS framework.

The Network Automation Integration Problem

You've got AI agents that need to troubleshoot network issues, gather device information, or apply configurations. But connecting AI safely to network infrastructure typically means:

• Writing custom REST APIs with proper validation
• Implementing security controls to prevent dangerous commands
• Managing device connections and credential handling
• Parsing and structuring network command outputs
• Building retry logic and error handling

The pyATS MCP Server eliminates this entire development cycle by wrapping pyATS/Genie in a secure, AI-friendly interface.

What Makes This Different

STDIN/STDOUT Communication: Unlike HTTP-based APIs, this server communicates through standard input/output using JSON-RPC 2.0. No open ports, no web server overhead, perfect for containerized environments and AI agent integration.

Built-in Safety: Input validation blocks dangerous commands (erase, reload, write), prevents command injection (|, >, copy), and validates all inputs through Pydantic schemas. Your network stays safe while AI agents operate autonomously.

Structured Data by Default: Returns parsed, structured data instead of raw text output. AI agents get JSON they can actually work with rather than having to parse CLI text.

Real-World AI Agent Scenarios

Automated Troubleshooting: Your monitoring system detects packet loss. An AI agent uses the MCP server to run show ip interface brief, ping tests, and show logging across affected devices, then correlates the structured data to identify the root cause.

Configuration Compliance: AI agents can audit device configurations by running show run brief, comparing against your desired state, and applying corrective configurations through the safe apply_configuration tool.

Network Discovery: During infrastructure changes, agents can discover topology by running show commands across your testbed devices and building comprehensive network maps from the structured responses.

Integration That Just Works

LangGraph Ready: Drop it into your LangGraph pipeline as a tool node:

("pyats-mcp", ["python3", "pyats_mcp_server.py", "--oneshot"], "tools/discover", "tools/call")

Container Native: The included Dockerfile makes deployment trivial. Mount your testbed file, set environment variables, and you're running network automation through AI agents.

One-Shot or Persistent: Run in continuous mode for long-lived agents, or one-shot mode for discrete tasks. The server adapts to your architecture.

Available Network Operations

The server exposes five core tools that cover most network automation needs:

• run_show_command: Execute any show command with optional Genie parsing
• run_ping_command: Network connectivity testing with structured results
• apply_configuration: Safe configuration changes with multi-line support
• learn_config: Fetch running configurations efficiently
• learn_logging: Retrieve system logs for troubleshooting

Each tool validates inputs and handles errors gracefully, so your AI agents get consistent, reliable responses.

Getting Started

1. Configure your testbed: Create a standard pyATS testbed.yaml with your device inventory
2. Set the environment: export PYATS_TESTBED_PATH=/path/to/testbed.yaml
3. Launch the server: python3 pyats_mcp_server.py
4. Connect your AI agent: The server accepts JSON-RPC requests via STDIN and responds via STDOUT

For containerized deployment:

docker run -i --rm \
  -e PYATS_TESTBED_PATH=/app/testbed.yaml \
  -v /your/testbed/folder:/app \
  pyats-mcp-server

Why This Matters Now

Network operations are moving toward AI-assisted workflows, but the integration layer has been the stumbling block. This MCP server provides the missing link between AI agents and network infrastructure, with the security and reliability you need for production environments.

The STDIO-based approach means you can embed network automation capabilities directly into AI agents without the complexity of managing HTTP services or dealing with network security concerns around exposed APIs.

Whether you're building AI-powered network operations tools or just want your existing automation to be more intelligent, the pyATS MCP Server gives you a head start with proven, production-ready network automation capabilities.