Outlit MCP Server

Query customer intelligence data through 8 MCP tools covering customer profiles, user activity, facts, semantic search, revenue metrics, and raw SQL analytics.

Quick Start — Which Tool to Use

What you need	Tool
Browse/filter customers	outlit_list_customers
Browse/filter users	outlit_list_users
Single customer deep dive	outlit_get_customer
What happened with a customer?	outlit_get_timeline
What do we know about a customer?	outlit_get_facts
Question about a customer or topic	outlit_search_customer_context
Custom analytics / aggregations	outlit_query (SQL)
Discover tables & columns	outlit_schema

Facts vs Search vs Timeline — When to Use Each

These three tools all surface customer context but serve different purposes:

Tool	Purpose	Example
outlit_get_facts	List all known facts about a customer with status/confidence	"Show me everything we know about Acme"
outlit_search_customer_context	Find relevant context for a specific question	"What has Acme said about pricing?"
outlit_get_timeline	See what happened in chronological order	"What happened with Acme last week?"

Rule of thumb: Use get_facts to browse, search_customer_context to answer questions, get_timeline to see chronology.

Before writing SQL: Always call outlit_schema first to discover available tables and columns.

Common Patterns

Find at-risk customers:

{
  "tool": "outlit_list_customers",
  "billingStatus": "PAYING",
  "noActivityInLast": "30d",
  "orderBy": "mrr_cents",
  "orderDirection": "desc"
}

Find context about a topic across all customers:

{
  "tool": "outlit_search_customer_context",
  "query": "data retention compliance concerns"
}

Revenue breakdown (SQL):

{
  "tool": "outlit_query",
  "sql": "SELECT billing_status, count(*) as customers, sum(mrr_cents)/100 as mrr_dollars FROM customer_dimensions GROUP BY 1 ORDER BY 3 DESC"
}

---

MCP Setup

Get an API Key

Go to Settings > MCP Integration in the Outlit dashboard (app.outlit.ai).

Auto-Detection Setup

Detect the current environment and run the appropriate setup command:

1. Check for Claude Code — If running inside Claude Code (check if claude CLI is available), run:

   claude mcp add outlit https://mcp.outlit.ai/mcp -- --header "Authorization: Bearer API_KEY"
   

2. Check for Cursor — If .cursor/mcp.json exists in the project or home directory, add to that file:

   {
     "mcpServers": {
       "outlit": {
         "url": "https://mcp.outlit.ai/mcp",
         "headers": { "Authorization": "Bearer API_KEY" }
       }
     }
   }
   

3. Check for Claude Desktop — If claude_desktop_config.json exists at ~/Library/Application Support/Claude/ (macOS) or %APPDATA%/Claude/ (Windows), add to that file:

   {
     "mcpServers": {
       "outlit": {
         "url": "https://mcp.outlit.ai/mcp",
         "headers": { "Authorization": "Bearer API_KEY" }
       }
     }
   }
   

Ask the user for their API key if not provided. Replace API_KEY with the actual key.

Verify Connection

Call outlit_schema to confirm the connection is working.

---

Tool Reference

outlit_list_customers

Browse and filter customers. Returns paginated list with summary info.

Key Params	Values
billingStatus	NONE, TRIALING, PAYING, CHURNED
hasActivityInLast / noActivityInLast	7d, 14d, 30d, 90d (mutually exclusive)
mrrAbove / mrrBelow	cents (10000 = $100)
search	name or domain
orderBy	last_activity_at, first_seen_at, name, mrr_cents
limit	1-1000 (default: 20)
cursor	pagination token

outlit_list_users

Browse and filter users. Returns paginated list with activity info.

Key Params	Values
journeyStage	DISCOVERED, SIGNED_UP, ACTIVATED, ENGAGED, INACTIVE
customerId	filter by customer
hasActivityInLast / noActivityInLast	Nd, Nh, or Nm (e.g., 7d, 24h) — mutually exclusive
search	email or name
orderBy	last_activity_at, first_seen_at, email
limit	1-1000 (default: 20)
cursor	pagination token

outlit_get_customer

Full details for a single customer. Accepts customer ID, domain, or name.

Key Params	Values
customer	customer ID, domain, or name (required)
include	users, revenue, recentTimeline, behaviorMetrics
timeframe	7d, 14d, 30d, 90d (default: 30d)

Only request the include sections you need — omitting unused ones is faster.

outlit_get_timeline

Chronological activity timeline for a customer.

Key Params	Values
customer	customer ID or domain (required)
channels	SDK, EMAIL, SLACK, CALL, CRM, BILLING, SUPPORT, INTERNAL
eventTypes	filter by specific event types
timeframe	7d, 14d, 30d, 90d, all (default: 30d)
startDate / endDate	ISO 8601 (mutually exclusive with timeframe)
limit	1-1000 (default: 50)
cursor	pagination token

outlit_get_facts

List all structured facts known about a customer. Use outlit_search_customer_context instead if you have a specific question.

Key Params	Values
customer	customer ID or domain (required)
timeframe	7d, 14d, 30d, 90d, all (default: 30d)
limit	1-100 (default: 50)
cursor	pagination token

outlit_search_customer_context

Semantic + full-text search over customer context (facts and emails). Use this to answer specific questions. Omit customer to search across all customers.

Key Params	Values
customer	customer ID, domain, or name (optional — omit for cross-customer search)
query	natural language question or topic (required)
topK	1-50 (default: 20)
occurredAfter / occurredBefore	ISO 8601 datetime bounds

outlit_query

Raw SQL against ClickHouse analytics tables. SELECT only. See SQL Reference for ClickHouse syntax and security model.

Key Params	Values
sql	SQL SELECT query (required)
limit	1-10000 (default: 1000)

Available tables: events, customer_dimensions, user_dimensions, mrr_snapshots.

outlit_schema

Discover tables and columns. Call with no params for all tables, or table: "events" for a specific table. Always call this before writing SQL.

---

Data Model

Billing status: NONE → TRIALING → PAYING → CHURNED

Journey stages: DISCOVERED → SIGNED_UP → ACTIVATED → ENGAGED → INACTIVE

Data formats:

• Monetary values in cents (divide by 100 for dollars)
• Timestamps in ISO 8601
• IDs with string prefixes (cust_, contact_, evt_)

Pagination: All list endpoints use cursor-based pagination. Check pagination.hasMore before requesting more pages. Pass pagination.nextCursor as cursor for the next page.

---

Best Practices

1. Call outlit_schema before writing SQL — discover columns, don't guess
2. Use customer tools for single lookups — don't use SQL for individual customer queries
3. Use search for questions, get_facts for browsing — search ranks by relevance, facts lists everything
4. Filter at the source — use tool params and WHERE clauses, not post-fetch filtering
5. Only request needed includes — omit unused include options for faster responses
6. Always add time filters to event SQL — WHERE occurred_at >= now() - INTERVAL N DAY
7. Convert cents to dollars — divide monetary values by 100 for display
8. Use LIMIT in SQL — cap result sets to avoid large data transfers

Known Limitations

1. SQL is read-only — no INSERT, UPDATE, DELETE
2. Organization isolation — cannot query other organizations' data
3. Timeline requires a customer — cannot query timeline across all customers
4. MRR filtering is post-fetch — may be slower on large datasets in list_customers
5. Event queries need time filters — queries without date ranges scan all data
6. ClickHouse syntax — uses different functions than MySQL/PostgreSQL (see SQL Reference)

---

Tool Gotchas

Tool	Gotcha
outlit_list_customers	hasActivityInLast and noActivityInLast are mutually exclusive
outlit_list_customers	search checks name and domain only
outlit_get_customer	behaviorMetrics depends on timeframe — extend it if empty
outlit_get_timeline	timeframe and startDate/endDate are mutually exclusive
outlit_search_customer_context	Omit customer to search across all customers
outlit_query	Use ClickHouse date syntax: now() - INTERVAL 30 DAY, not DATE_SUB()
outlit_query	properties column is JSON — use JSONExtractString(properties, 'key')

---

References

Reference	When to Read
SQL Reference	ClickHouse syntax, security model, query patterns
Workflows	Multi-step analysis: churn risk, revenue dashboards, account health