Jiminny MCP | API Key Jiminny connector for AI agents

Tools your sales intelligence agent reaches for on Jiminny, scoped per user.

CALL ANY TOOL

Search transcripts, retrieve AI insights, pull talk ratios, and surface coaching opportunities.

jiminny_users_list

Users list

Retrieve all users belonging to the authenticated team, including their IDs, names, emails, statuses, team names, CRM IDs, and roles.

Parameters

Name

Type

Required

Description

—

No parameters required

jiminny_summary_get

Summary get

jiminny_listens_list

Listens list

jiminny_activity_get

Activity get

jiminny_comments_list

Comments list

jiminny_questions_get

Questions get

jiminny_webhooks_list

Webhooks list

jiminny_transcript_get

Transcript get

jiminny_webhook_create

Webhook create

jiminny_webhook_delete

Webhook delete

jiminny_activities_list

Activities list

jiminny_activity_upload

Activity upload

jiminny_organization_get

Organization get

jiminny_test_tool_xyz

Test tool xyz

jiminny_action_items_get

Action items get

jiminny_ai_scorecards_list

Ai scorecards list

jiminny_ai_scorecard_get

Ai scorecard get

jiminny_topic_triggers_list

Topic triggers list

jiminny_webhook_sample_get

Webhook sample get

jiminny_automated_reports_list

Automated reports list

jiminny_automated_report_get

Automated report get

jiminny_coaching_feedback_list

Coaching feedback list

jiminny_zapier_activity_upload

Zapier activity upload

jiminny_topic_triggers_matched_get

Topic triggers matched get

jiminny_automated_call_scoring_list

Automated call scoring list

jiminny_automated_report_status_get

Automated report status get

jiminny_automated_report_download_get

Automated report download get

Build your Agent

Drop the toolkit in, point it at the user, and your sales intelligence agent can use Jiminny from the first run.

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: ["jiminny"], toolNames: ["jiminny_calls_list", "jiminny_call_get", "jiminny_call_transcript"] },
  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 });

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: ["jiminny"], toolNames: ["jiminny_calls_list", "jiminny_call_get", "jiminny_call_transcript"] },
  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: ["jiminny"], toolNames: ["jiminny_calls_list", "jiminny_call_get", "jiminny_call_transcript"] },
  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 }],
});

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/jiminny",
    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 Jiminny.

Search & recall

Copy the prompt

Copied

Find calls mentioning [competitor] this week.

Copy the prompt

Copied

List all calls with [account name].

Copy the prompt

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Show transcripts where budget came up.

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Which reps had the most calls today?

Insights & coaching

Copy the prompt

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Get AI insights for [call name].

Copy the prompt

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What was the talk-to-listen ratio for [rep]?

Copy the prompt

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Summarize next steps from [call].

Copy the prompt

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List calls with low engagement scores.

Reporting

Copy the prompt

Copied

Compare call volumes by rep this month.

Copy the prompt

Copied

Which accounts have the most recorded calls?

Copy the prompt

Copied

Find calls where pricing objections came up.

Copy the prompt

Copied

List calls with no next step committed.

SEE HOW AUTH WORKS

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

Authorize

Your user connects

Jiminny

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

Jiminny

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

Jiminny

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

Jiminny

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 sales intelligence agents and MCP connectors. Working code, live demos, fork what fits.

GTM and RevOps Teams

CRM AI agent

Reads the Granola transcript after every call, extracts next steps and updates the HubSpot record, drafts the follow-up in Gmail, and confirms in Slack, all on the rep's own delegated OAuth.

GTM and RevOps Teams

Sales call prep agent

Reads tomorrow's calls from Google Calendar, mines past Granola notes and Attio history for context, and delivers each rep a prep brief in Slack, scoped to the calls that rep actually owns.

GTM and RevOps Teams

Deal intelligence agent

Pulls recent Gong calls, scores deal risk with an LLM, cross-references the record in Attio, and DMs each owner their at-risk deals in Slack. Every read is scoped to that rep's own access.

GTM and RevOps Teams

Competitive intelligence briefing agent

Scans Gong calls for competitor mentions, matches each one to its Notion battlecard, and DMs every affected rep a single Slack digest per cycle. Every call runs as the PMM who owns the briefing, never a shared bot.

Why Scalekit

Secure your agent's access. Connectors ship in minutes

01.

Shared tokens break per-user analytics

A shared token looks fine in a demo. In production every call looks like a service account. Scalekit resolves the real user credential so attribution, audit, and scope stay accurate.

// shared token
 audit → bot_service_account
 user_filter → broken

 // scalekit
 audit → user_abc
 scope → enforced ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

Jiminny 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

FAQs

Frequently Asked Questions

Does the agent access Jiminny 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 Jiminny api key stored?

In Scalekit's managed AES-256 token vault, namespaced per tenant. Refresh is automatic. Revocation is a single dashboard action. Tokens never appear in prompts, logs, or LLM context.

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

Yes. Pass a tool name filter to listScopedTools so the sales intelligence agent only sees the subset you authorize. Pre-API-call scope checks block out-of-policy actions before the request reaches Jiminny.

What happens when a user revokes Jiminny access?

The connection is invalidated on the next tool call. Subsequent requests for that user fail closed with a clear error. Other users in the tenant remain unaffected. The event is logged for audit.

Which calls can the agent surface insights for?

Only calls the authorizing user can see in Jiminny. Manager-level access surfaces team calls. Rep-level access stays scoped to that rep's own recordings and any explicitly shared calls.

On this page

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