Evertrace AI MCP | Api Key Evertrace AI connector for AI agents

Tools your analytics agent reaches for on Evertrace AI, scoped per user.

CALL ANY TOOL

Query funnels, list events and sessions, filter users by behavior, and pull saved reports.

evertrace_events_list

List events

List tracked events with name, count, and date range filters.

Parameters

Name

Type

Required

Description

event_name

string

Optional

Filter by event name

from_date

string

Optional

ISO 8601 start date

to_date

string

Optional

ISO 8601 end date

limit

integer

Optional

Max events

evertrace_sessions_list

List sessions

evertrace_funnels_query

Query funnel

evertrace_users_list

List users

evertrace_reports_get

Get report

Build your Agent

Drop the toolkit in, point it at the user, and your analytics agent can use Evertrace AI from the first run.

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import { ScalekitClient } from "@scalekit-sdk/node";
import { DynamicStructuredTool } from "@langchain/core/tools";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { z } from "zod";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["evertrace"], toolNames: ["evertrace_events_list", "evertrace_sessions_list", "evertrace_funnels_query"] },
  pageSize: 100,
});

const lcTools = tools.map((t) => new DynamicStructuredTool({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  schema: z.object({}).passthrough(),
  func: async (args) => {
    const { data } = await sk.tools.executeTool({
      toolName: t.tool.definition.name,
      identifier: "user_123",
      params: args,
    });
    return JSON.stringify(data);
  },
}));

const agent = createReactAgent({ llm, tools: lcTools });

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import { ScalekitClient } from "@scalekit-sdk/node";
import OpenAI from "openai";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const openai = new OpenAI();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["evertrace"], toolNames: ["evertrace_events_list", "evertrace_sessions_list", "evertrace_funnels_query"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  type: "function",
  function: {
    name: t.tool.definition.name,
    description: t.tool.definition.description,
    parameters: t.tool.definition.input_schema,
  },
}));

const resp = await openai.responses.create({
  model: "gpt-4o", input: prompt, tools: llmTools,
});

import { ScalekitClient } from "@scalekit-sdk/node";
import Anthropic from "@anthropic-ai/sdk";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const anthropic = new Anthropic();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["evertrace"], toolNames: ["evertrace_events_list", "evertrace_sessions_list", "evertrace_funnels_query"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  input_schema: t.tool.definition.input_schema,
}));

const msg = await anthropic.messages.create({
  model: "claude-sonnet-4-6", max_tokens: 1024,
  tools: llmTools,
  messages: [{ role: "user", content: prompt }],
});

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import { Agent } from "@google/adk/agents";
import {
  MCPToolset, StreamableHTTPConnectionParams,
} from "@google/adk/tools/mcp";

const toolset = new MCPToolset({
  connectionParams: new StreamableHTTPConnectionParams({
    url: "https://mcp.scalekit.com/evertrace",
    headers: { Authorization: `Bearer ${userScopedToken}` },
  }),
});

const agent = new Agent({
  name: "agent", model: "gemini-2.0-flash",
  tools: await toolset.getTools(),
});

Try these prompts

Paste any prompt into your agent to start using Evertrace AI.

Search & recall

Copy the prompt

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List all tracked events from this week.

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Show me sessions longer than 5 minutes.

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Find users who completed [event] but not [event].

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Get report [report name].

Funnel & conversion

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Query funnel: [step 1] → [step 2] → [step 3].

Copy the prompt

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What's the conversion rate from signup to activation?

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Where do users drop off in the [flow name] flow?

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Compare funnel by acquisition source.

Reporting & retention

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Daily active users for the last 30 days.

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List users who churned this month.

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Show top events by volume today.

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Which users performed [event] 3+ times this week?

SEE HOW AUTH WORKS

Users authorize Evertrace AI once. Their credentials stay vaulted, every call is checked, and every action is logged.

Authorize

Your user connects

Evertrace AI

once. We tie it to their identity and the meetings they approved — no shared bot account, no org-wide access

Who:

user ‘A’

when:

Once per user

access:

Limited to user

Store

Their

Evertrace AI

token lives in a vault scoped to them. User A's meetings are never reachable by an agent acting for user B, even on the same connection

vault:

encrypted

scope:

per-user

tokens:

auto-refreshed

Resolve

When your agent calls a

Evertrace AI

tool, we fetch the right token server-side. It never touches your agent, never appears in the LLM context, never shows up in your logs

speed:

~40ms

check:

before every call

seen by:

nobody

Audit

Every

Evertrace AI

tool call is logged — who triggered it, which meeting was fetched, what came back. 90 days of history, tied to the user who authorized it

history:

90 days

export:

SIEM-ready

logged:

every call

Test other agents

Same per-user auth pattern across other analytics agents and MCP connectors. Working code, live demos, fork what fits.

SALES

Outbound prospecting agent

Build targeted prospect lists with Apollo, enrich with firmographic data, and draft personalised outreach. Runs on a schedule.

Apollo

View agent

GTM

HubSpot to Slack updates agent

Watch HubSpot deal stage changes and post structured updates to the right Slack channel. Reps stop checking the CRM all day.

HubSpot

Slack

View agent

Why Scalekit

Secure your agent's access. Connectors ship in minutes

Other connector libraries treat auth as a demo afterthought. Scalekit starts with user identity, scope enforcement, and audit.

01.

Analytics events queried from the wrong project identity

A shared Evertrace API key looks fine in a demo. In production, every funnel query and event lookup is attributed to the service account. Project-level isolation breaks. Scalekit resolves the user's key so queries run under the right project identity.

// shared API key
key = "evt_evertrace_shared_xxx"
audit → bot_service_account
project_filter → broken

// scalekit · per-user
key = resolve(user_id)
audit → user_abc
scope → enforced ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

Evertrace AI today. Others tomorrow.

“Our agents act across Salesforce, Gong, Google Drive, and more, on behalf of every customer. Scalekit behind the scenes meant we can keep adding tools without ever rebuilding how credentials or tool calling work.”

Venu Madhav Kattagoni

Head of Engineering / Von

On this page

Tool and actions Framework Compatibility Starter Prompt Auth Lifecycle Try Other Agents Why Scalekit

Frequently Asked Questions

Does the agent access Evertrace AI as the user or as a shared key?

As the user. Each workspace member authorizes once and Scalekit resolves their credential at request time. Audit logs attribute every action to that user, not a shared service account.

Where is the Evertrace AI api key stored?

Can I limit what the agent is allowed to do in Evertrace AI?

What happens when a user revokes Evertrace AI access?

Can the agent query events across multiple Evertrace projects?

Start in your coding agent

Up and running in one command

Install the Scalekit skill in your editor of choice. Connector, auth, tools, prompt, all wired up

Claude Code REPL

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/plugin marketplace add scalekit-inc/claude-code-authstack
/plugin install agentkit@scalekit-auth-stack

Cursor Code REPL

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# ~/.cursor/mcp.json
{
""mcpServers"": {
""evertrace"": {
""url"": ""https://mcp.scalekit.com/evertrace"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}

Codex Code REPL

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# ~/.codex/config.toml
[mcp_servers.evertrace]
url = ""https://mcp.scalekit.com/evertrace""
auth_env = ""SCALEKIT_TOKEN""

Copilot Code REPL

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# .vscode/mcp.json
{
""servers"": {
""evertrace"": {
""url"": ""https://mcp.scalekit.com/evertrace"",
""type"": ""http""
}
}
}