Which integrations are available for this server?

Provides IVR flow validation tools for ChatGPT, enabling linting of IVR JSON flows with error detection, scoring, and interactive Skybridge widget visualization of results.

How do I use IVR Flow Linter?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type `@` followed by the MCP server name and your instructions, e.g., `@IVR Flow Linter lint this IVR flow JSON for errors and suggest fixes` That's it! The server will respond to your query, and you can continue using it as needed. Here is a [step-by-step guide with screenshots](https://glama.ai/blog/2025-07-08-how-to-install-and-use-mcp-servers). You can also use deployed servers via HTTP endpoints. For instructions, see [How to Test MCP Streamable HTTP Endpoints Using cURL](https://glama.ai/blog/2026-01-02-how-to-test-mcp-streamable-http-endpoints-using-c-url).

IVR Flow Linter

Build Status Python Version License

Deterministic IVR Flow Linter via MCP + ChatGPT Skybridge Widget

A Model Context Protocol (MCP) server that validates IVR flows (JSON) providing a score, error detection (unreachable nodes, dead ends), and suggested fixes. Includes a Skybridge-pattern HTML widget for visualizing results directly in ChatGPT.

Demo Video

Widget Screenshot

Features

Strict Validations: Unreachable nodes, dead ends, missing fallbacks, undefined variables.
Best Practices: Warning on long prompts, multiple questions, and irreversible actions without confirmation.
Idempotency: Consistent results via flow hashing.
Skybridge Widget: Interactive HTML/JS visualization with zero external dependencies.
MCP Compatible: Works with both ChatGPT Actions and Claude Desktop.

Architecture

Server: FastAPI with JSON-RPC 2.0 (/mcp)
Linter: Pure Python deterministic engine
Widget: Inline HTML injection (text/html+skybridge)

Local Development

Prerequisites

Python 3.9+
git

Setup

# Clone git clone https://github.com/arebury/ivr-flow-linter.git cd ivr-flow-linter # Virtual Environment python -m venv venv source venv/bin/activate # Install pip install -r requirements.txt # Run Server uvicorn src.main:app --host 0.0.0.0 --port 8000 --reload

Visit http://localhost:8000/demo for an interactive simulator.

Validating the Demo

Run server: uvicorn src.main:app --host 0.0.0.0 --port 8000 --reload
Open http://localhost:8000/demo
Select "Invalid: Dead End" from the dropdown.
Click Lint & Render.
Verify:
- "Parsed Result" tab shows errors.
- Widget renders the flow with error indicators.

Deployment (Render)

This project includes a render.yaml for 1-click deployment on Render.

Create a new Web Service on Render.
Connect your GitHub repository: https://github.com/arebury/ivr-flow-linter.
Select "Python 3" runtime.
Build Command: pip install -r requirements.txt
Start Command: uvicorn src.main:app --host 0.0.0.0 --port $PORT

Connecting to ChatGPT (Apps SDK / MCP)

This server acts as an MCP Server that exposes the lint_flow tool. The tool returns a Skybridge-compatible widget (text/html+skybridge) for rich visualization.

Deploy the service to a public URL (e.g., https://your-app.onrender.com).
Register the app in the OpenAI Developer Portal (for Apps SDK) using the manifest at /.well-known/ai-plugin.json.
Alternatively, for Custom Actions (GPTs):
- Import configuration from https://your-app.onrender.com/openapi.yaml.
- Ensure the model knows how to interpret the ui field in the response (Skybridge pattern).

Connecting to Claude (Desktop)

Add the server to your Claude Desktop configuration (claude_desktop_config.json):

Local:

{ "mcpServers": { "ivr-linter": { "command": "uvicorn", "args": ["src.main:app", "--port", "8000"], "cwd": "/absolute/path/to/ivr-flow-linter" } } }

Remote (via Stdio Wrapper - Advanced): (Typically Claude Desktop connects to local processes. For remote MCP, use a local bridge or simple local-proxy).