New: Model Context Protocol Support

The rConfig MCP server
Code meets configuration

Tap into the Model Context Protocol to generate scripts, automate workflows, and explore configurations securely using your AI coding assistant. It is the outbound half of our network AI maturity model.

01. Why it matters

AI for network reliability

Bridge the gap between human intent and machine execution with the power of the Model Context Protocol.

  • Configuration drift

    AI-assisted detection of unintended changes across your fleet, with corrections proposed for human approval.

  • Automated scripts

    Turn natural language into ready-to-run automation code for Python, Bash, or Ansible.

  • Open developer access

    AI can browse, query, and take governed, permissioned actions on rConfig’s open-source foundation through the standardised MCP.

  • Private & secure

    Absolutely no data leaves your environment. The MCP server runs locally within your infrastructure.

02. Capabilities

How MCP-AI works

Four stages, connect, engineer, explore, govern, each built to surface context without ever leaving the customer’s rConfig instance.

Feature 01

Connect your AI tools

Seamlessly integrate with Claude Code, Cursor, and ChatGPT. The MCP standard allows these models to automatically discover rConfig's capabilities without complex manual configuration.

  • Native Claude & Cursor integration
  • Automatic tool discovery via MCP
  • Multi-model orchestration support
Claude
linking…
Cursor
idle
ChatGPT
idle
MCP
rconfig v8
0/3 clients
Establishing MCP Handshake...
rConfig MCP
Feature 02

Intelligent engineering

Create backup scripts, diff API requests, and reporting pipelines in seconds. Just describe what you need, and watch the code appear, ready for review and secure execution.

  • Precision script generation
  • Real-time API diffing & testing
  • Governed workflow automation
# prompt · Find devices with SNMPv2 enabled and generate a remediation script.
audit_ntp.py
generating
+14−2audit_ntp.py0/7 lines
Generating Contextual Scripts...
rConfig MCP
Feature 03

Explore rConfig’s core

Your AI assistant can securely browse models, documentation, and configuration metadata, giving it the specific context it needs to provide accurate, network-aware answers.

  • Secure metadata indexing
  • Deep configuration exploration
  • Live documentation grounding
0/6 schemas · 0%
/schema/device
fields: id, hostname, vendor…
Indexing rConfig Schema...
rConfig MCP
Feature 04

Enterprise governance

Full RBAC inheritance with a strict separation between read-safe and write-class operations. Read-safe is the default. Any write-class action is permissioned, human-approved, and logged for full audit compliance.

  • Strict RBAC inheritance
  • Read-safe by default, writes gated
  • Full audit logging for AI sessions
Policy Engine
enforced · rconfig-rbac
0/3 active
READ-SAFEconfigs · templates
,
WRITE GATEapproval required
,
AUDIT STREAMlive · signed
,
Verifying Permission Scopes...
rConfig MCP
The rConfig Skill · 3 benefits

AI as a Skill, built into your network toolkit

Download rConfig as a Skill and your AI coding tools gain network-aware context and governed actions, without bespoke integration work.

  • A packaged capability, not just an endpoint

    The MCP server is the live endpoint. The rConfig Skill is the packaged capability your AI tools load to learn how to drive rConfig, distinct from the server and portable across tools.

  • Loaded by your AI coding tools

    Claude Code, Cursor, ChatGPT and other MCP-aware tools load the rConfig Skill to gain network-aware context and a governed set of actions, scoped by your RBAC.

  • Automatic discovery, no manual wiring

    Through the MCP standard the tool discovers rConfig's capabilities for itself, so the developer gets network context without complex manual configuration.

This is the outbound story, rConfig exposed to your own AI tools. For AI inside rConfig that reads and explains your estate, see AI config analysis.

Model Context Protocol, Under the Hood

Exposed Toolset

rConfig exposes a curated “toolset” to AI models via MCP. These tools describe available API calls, schemas, and safe operations in a format LLMs natively understand.

  • Describe API definitions & schemas
  • Distinguish safe vs. unsafe operations
  • Context-aware config inspection
MCP Flow Execution
running
User Request: "Audit switch configs"
02AI discovers `get_device_config` tool
03AI generates API Payload
04rConfig executes in Sandbox
05Result returned to context window
Latency
,
Sandbox
idle
Tokens
,

All executed inside the customer’s rConfig instance. No external data egress.

03. Process

The workflow

From prompt to production in five reviewable, audit-logged steps.

1

Natural-language request

Ask your AI assistant to perform a task.

2

Tool discovery

AI identifies available rConfig MCP tools.

3

Code generation

AI constructs the necessary API calls or scripts.

4

Review & run

You validate the logic, then execute securely.

5

Audit logged

Full traceability of the AI’s actions.

04. In practice

Who uses MCP-AI

Same underlying protocol, three different jobs, each team gets configuration-aware automation phrased for their workflow.

  • System integrators

    Build and maintain complex automations faster. Generate boilerplate integration code for third-party tools instantly.

  • MSPs & DevOps

    Generate multi-tenant onboarding scripts and compliance reports tailored to specific customer environments safely.

  • Automation engineers

    Turn plain-language directives into Python, Bash, PowerShell, or PHP pipelines without manual syntax lookup.

FAQ

Frequently Asked Questions

Bridge your AI with your Infrastructure

Deploy the rConfig MCP Server to give your AI assistants direct, governed access to your network data. Secure, scalable, and built for the enterprise.

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