Idealift MCP Server
Decision intelligence with signal aggregation, RICE scoring and decision audit trails, capturing product feedback from Slack, Teams, Discord and GitHub.
What can you do with Idealift MCP Server?
- Normalize ideas — Transform raw text into structured feature requests, bug reports, or tasks
- Signal aggregation — Capture product feedback from Slack, Teams, Discord, and GitHub
- Decision tracking — Record who decided what, when, and why with full audit trails
- RICE scoring — Prioritize ideas with Reach, Impact, Confidence, and Effort scores
- Ticket creation — Push structured ideas to GitHub Issues, Jira, or Linear
Install
The installation steps are in the documentation below. Documentation
Documentation
From the project's README on GitHub, refreshed weekly. Links open on GitHub.
IdeaLift MCP Server
Decision intelligence for AI assistants via Model Context Protocol.
Connect Claude, ChatGPT, and other AI assistants to your product backlog. Capture feedback, track decisions, and manage ideas without leaving your AI workflow.
What it does
- Normalize ideas — Transform raw text into structured feature requests, bug reports, or tasks
- Signal aggregation — Capture product feedback from Slack, Teams, Discord, and GitHub
- Decision tracking — Record who decided what, when, and why with full audit trails
- RICE scoring — Prioritize ideas with Reach, Impact, Confidence, and Effort scores
- Ticket creation — Push structured ideas to GitHub Issues, Jira, or Linear
MCP Tools
| Tool | Auth Required | Description |
|---|---|---|
normalize_idea | No | Transform raw text into a structured idea |
check_auth | No | Check if user is linked to IdeaLift |
list_destinations | Yes | Get available GitHub/Jira/Linear projects |
create_ticket | Yes | Create a ticket in a destination system |
connect_destination | Yes | Connect a new destination |
Plus proxy tools for full IdeaLift API access (ideas, signals, decisions, roadmap).
Setup
1. Install dependencies
npm install2. Configure environment
cp .env.example .envRequired environment variables:
| Variable | Description |
|---|---|
IDEALIFT_APP_URL | IdeaLift API base URL |
INTERNAL_API_KEY | Service-to-service auth key |
DATABASE_HOST | SQL Server host |
DATABASE_NAME | Database name |
DATABASE_USERNAME | Database user |
DATABASE_PASSWORD | Database password |
OPENAI_API_KEY | For idea normalization |
3. Build and run
npm run build
npm startOr for development:
npm run devThe MCP server starts on port 3001 (configurable via PORT).
4. Connect to your AI assistant
SSE endpoint: http://localhost:3001/mcp
Add this URL as an MCP server in Claude Desktop, ChatGPT, or any MCP-compatible client.
Transport
Uses Server-Sent Events (SSE) transport with automatic keep-alive pings every 15 seconds.
OAuth
Built-in OAuth 2.0 flow for connecting AI assistant users to their IdeaLift accounts. Supports authorization code grant with PKCE.
Deployment
Deployed as an Azure App Service. See the IdeaLift docs for hosted access.
License
MIT
Frequently asked questions
What is Idealift MCP Server?
Decision intelligence with signal aggregation, RICE scoring and decision audit trails, capturing product feedback from Slack, Teams, Discord and GitHub.
How do I install Idealift MCP Server?
Follow the installation steps in its documentation, shown on this page and in its repository.
Is Idealift MCP Server open source?
Yes. Its code is on GitHub under the MIT license.
Who maintains Idealift MCP Server?
It is published by Startvest-LLC. AI.tn lists it but does not maintain or endorse it.
From the community awesome-mcp-servers lists and the project's GitHub repository. Listing is not an endorsement: review a server's code and permissions before giving it access to your data.