cognigy-ai-mcp-management-server
This server enables programmatic management of Cognigy.AI virtual agents, allowing AI assistants to build, configure, test, and operate agents via natural language or API calls through 132 MCP tools.
Project & Flow Management: List projects, list/get flows, get flow settings, and retrieve latest log entries.
Node Operations: List, get, search, create, update, delete, and move nodes; generate AI-powered node content (e.g., Say messages or Adaptive Cards).
Intent & NLU: CRUD intents, manage example sentences, generate new sentences via AI, train NLU models, score utterances, audit NLU quality, run regression tests.
Endpoint & Session Management: List/get endpoints, inject/reset session context, fetch conversations and transcripts.
Testing & Playbooks: List/get/run playbooks, review runs, execute regression suites.
Snapshots & Packages: Create, delete, restore, package, download, upload, diff, and promote snapshots; create, merge, upload, download, delete packages; clone flows.
Connections & LLMs: Manage API connections (secrets redacted), configure and test LLM providers, clone/delete LLMs.
NLU Connectors: Integrate external NLU services (Dialogflow, LUIS, Watson, Alexa, Lex, custom code).
Knowledge AI (RAG): Manage knowledge stores, sources, chunks, connectors for RAG; create/update/delete/run connectors.
Functions & Extensions: List/get custom code functions; upload/configure/manage extensions.
Contact Profiles & Analytics: Manage contact profiles (CRUD, merge, export for GDPR) and access analytics metrics.
Audit & Search: Retrieve audit event logs and perform organization-wide resource search.
Async Task Monitoring: List and get status of long-running tasks (training, snapshot creation, etc.) with polling support.
Safety: All mutating tools default to
dryRun: trueto validate operations before applying changes; secrets are redacted in responses.
Allows configuration and management of external NLU connectors such as Dialogflow for natural language understanding in Cognigy.AI virtual agents.
Cognigy.AI Management MCP Server

Model Context Protocol server for managing Cognigy.AI virtual agents through the Management API
This is an independent, open-source MCP server and is not affiliated with, endorsed by, or sponsored by Cognigy or NiCE. It requires your own valid Cognigy.AI account and API key, used in accordance with Cognigy's commercial license terms. "Cognigy" and "Cognigy.AI" are trademarks of their respective owners.
What is this?
cognigy-ai-mcp-management-server is a local MCP server that enables AI coding assistants (Claude, Cursor, etc.) to build, configure, test, and operate Cognigy.AI conversational AI agents programmatically.
Instead of clicking through the Cognigy UI, you can now:
Create and manage flows, nodes, and intents via natural language
Run NLU training and regression tests automatically
Deploy snapshots and packages across environments
Configure LLMs, Knowledge AI, and integrations
Built for: Developers, solution architects, and SI partners who build on Cognigy.AI.
Related MCP server: MCP Starter
Requirements
Node.js 20.0.0 or higher
Cognigy.AI account with API access (Trial, SaaS, or on-premises)
API Key generated from Cognigy UI (My Profile → API Keys)
Installation
# 1. Install the Cognigy REST API client (required peer dependency)
npm install @cognigy/rest-api-client
# 2. Install this MCP server
npm install -g cognigy-ai-mcp-management-serverImportant: The @cognigy/rest-api-client package is licensed under "Cognigy Proprietary License". By installing it, you agree to Cognigy's license terms.
Configuration
Set environment variables before running:
# Required: Your Cognigy API endpoint
export COGNIGY_BASE_URL=https://api-trial.cognigy.ai
# Required: Your API key (never commit this!)
export COGNIGY_API_KEY=your-api-key-here
# Optional: Default project for operations
export COGNIGY_DEFAULT_PROJECT_ID=your-project-idCommon Base URLs:
Environment | Base URL |
Trial |
|
SaaS EU |
|
SaaS US |
|
Dedicated |
|
MCP Client Configuration
Claude Desktop
Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"cognigy": {
"command": "npx",
"args": ["cognigy-ai-mcp-management-server"],
"env": {
"COGNIGY_BASE_URL": "https://api-trial.cognigy.ai",
"COGNIGY_API_KEY": "your-api-key-here"
}
}
}
}Claude Code
Add to .mcp.json in your project root:
{
"mcpServers": {
"cognigy": {
"command": "npx",
"args": ["cognigy-ai-mcp-management-server"],
"env": {
"COGNIGY_BASE_URL": "https://api-trial.cognigy.ai",
"COGNIGY_API_KEY": "your-api-key-here"
}
}
}
}Cursor
Add to .cursor/mcp.json:
{
"mcpServers": {
"cognigy": {
"command": "npx",
"args": ["cognigy-ai-mcp-management-server"],
"env": {
"COGNIGY_BASE_URL": "https://api-trial.cognigy.ai",
"COGNIGY_API_KEY": "your-api-key-here"
}
}
}
}Available Tools
This server provides 132 MCP tools organized by domain:
Category | Tools | Description |
Projects & Flows | 5 | List projects, flows, flow settings, logs |
Nodes | 9 | CRUD operations, search, move, AI output generation |
Intents & NLU | 10 | Intent management, training, scoring, audit |
Endpoints | 2 | List and inspect endpoint configurations |
Sessions | 2 | Inject/reset conversation context |
Conversations | 3 | Fetch conversations, transcripts |
Playbooks & Testing | 6 | Run playbooks, regression tests |
Snapshots | 9 | Create, restore, package, diff, promote |
Packages | 7 | Create, merge, upload, download |
Connections | 5 | Manage API connections (secrets redacted) |
LLMs | 7 | Configure generative AI providers |
NLU Connectors | 5 | External NLU (Dialogflow, LUIS, etc.) |
Knowledge AI | 21 | Stores, sources, chunks, connectors (RAG) |
Functions | 9 | Custom code functions, instances |
Extensions | 6 | Upload, configure, manage extensions |
Contact Profiles | 11 | Profile CRUD, merge, export (GDPR) |
Analytics | 4 | Conversation, call, knowledge metrics |
Audit | 2 | Audit event logs |
Handover | 7 | Live agent providers and services |
Search | 1 | Organization-wide resource search |
Tasks | 2 | Async task status tracking |
Key Features
Safe by default: All mutating tools use
dryRun: trueby defaultAsync-aware: Long-running operations poll until completion
Security: API keys only in memory, secrets automatically redacted
Pagination: All list operations support
limitandskip
Development & Testing
Mock-first Development
Run against Prism mock server (no Cognigy account needed):
# Terminal 1: Start mock server
npm run mock
# Terminal 2: Run tests
npm testLive API Testing
# Create .env from template
cp .env.example .env
# Edit .env with your credentials
# Run development server
npm run devBuild
npm run build # Compile TypeScript
npm test # Run test suite (49 tests)
npm run lint # Check code styleRegenerate Types (optional)
If you need to update the generated types from a newer Cognigy API:
npm run update:spec # Download latest OpenAPI spec
npm run gen:types # Regenerate TypeScript typesImportant: Dependencies
This package requires @cognigy/rest-api-client as a peer dependency. This means:
You must install it separately (see Installation above)
You accept Cognigy's license terms by installing their package
We do not bundle or redistribute any Cognigy code
You are responsible for:
Reviewing and accepting Cognigy's proprietary license terms
Having appropriate rights to use the Cognigy API
Keeping your API credentials secure
The Cognigy REST API client is the official SDK maintained by Cognigy GmbH and is published on npm under "Cognigy Proprietary License".
License
This MCP server is released under the MIT License. See LICENSE for details.
Note: The MIT license applies only to this MCP server code. Dependencies (particularly @cognigy/rest-api-client) are subject to their own licenses.
Contributing
Contributions are welcome! Please:
Fork the repository
Create a feature branch (
git checkout -b feature/amazing-feature)Follow existing code patterns (Zod schemas, dryRun flags, etc.)
Add tests for new tools
Run
npm test && npm run buildbefore committingSubmit a Pull Request
Development Guidelines
All tools must have Zod input validation
Mutating tools must support
dryRunflag (default: true)Tool descriptions should be LLM-friendly (1-2 sentences)
Never log or expose API keys/secrets
Test against mock server before live API
Support
Issues: GitHub Issues
Cognigy Docs: docs.cognigy.com
MCP Spec: modelcontextprotocol.io
Built with Model Context Protocol SDK
Maintenance
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