A specialized MCP server for querying customer intelligence, revenue metrics, and user activity directly within AI-powered development environments.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install outlit-mcp
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install outlit-mcp using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Outlit MCP Server?
The Outlit MCP Server is a robust integration designed to provide AI agents with direct access to customer intelligence data. By leveraging this Openclaw Skills resource, developers can interact with customer and user profiles, monitor revenue metrics, and track activity timelines through a standardized interface. It bridges the gap between raw data and actionable insights by allowing agents to perform deep-dives into customer health and business growth.
This skill enables a seamless workflow for growth teams and developers who need to synthesize customer data without leaving their AI-enabled IDEs. Whether you are assessing churn risk or building revenue dashboards, the Outlit MCP tools provide the necessary data points through structured API calls and raw SQL capabilities.
Outlit MCP Server Use Cases
- Identifying at-risk customers by filtering for high MRR with low recent activity
- Generating revenue breakdowns and MRR snapshots via SQL analytics
- Performing deep-dive customer audits including user journey stages and behavior metrics
- Monitoring multi-channel activity history across SDK, Email, Slack, and Support channels
- Discovering database schemas to build custom customer health reports
How Outlit MCP Server Works
- The AI agent connects to the Outlit MCP server using a secure API key provided in the configuration.
- The agent utilizes discovery tools like outlit_schema to understand the underlying data structure and available tables.
- Based on user prompts, the agent selects the most efficient tool, such as outlit_list_customers for filtering or outlit_query for complex aggregations.
- The server processes the request against Outlit's data engine, which includes ClickHouse-backed analytics.
- Structured data is returned to the agent, which then interprets the results to provide answers, visualizations, or strategic recommendations.
Outlit MCP Server Setup
To get started with this Openclaw Skills integration, follow these steps:
- Obtain an API Key from Settings > MCP Integration in your Outlit dashboard.
- For Claude Code, run:
claude mcp add outlit https://mcp.outlit.ai/mcp -- --header "Authorization: Bearer YOUR_API_KEY"
- For Cursor, add the following to your
.cursor/mcp.json:
{
"mcpServers": {
"outlit": {
"url": "https://mcp.outlit.ai/mcp",
"headers": { "Authorization": "Bearer YOUR_API_KEY" }
}
}
}
- For Claude Desktop, add the configuration block to your
claude_desktop_config.jsonfile. - Verify the connection by calling
outlit_schema.
Outlit MCP Server Data Schema & Taxonomy
The skill organizes data around the following primary entities and tables:
| Table | Description |
|---|---|
customer_dimensions |
Core customer metadata, billing status, and MRR |
user_dimensions |
Individual user profiles and journey stages |
events |
Raw activity data with JSON properties column |
mrr_snapshots |
Historical revenue data for trend analysis |
Data Format Notes:
- IDs: Prefixed with
cust_(customers),contact_(users), orevt_(events). - Monetary: Values are in cents (e.g., 10000 = $100.00).
- Timestamps: Standard ISO 8601 format.
Outlit MCP Server Advanced Features
- Raw SQL access to ClickHouse analytics tables for complex, read-only data exploration
- Cursor-based pagination allowing for efficient retrieval of large customer lists
- Multi-channel timeline filtering (SDK, CRM, Slack, Support, and Billing)
- Behavior metrics integration that calculates engagement levels over custom timeframes
- Automatic schema discovery to ensure SQL queries remain compatible with the latest data models
SKILL.md
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