Jiminny

Live

API KEY

CALL INTELLIGENCE

Every recorded sales call, AI insight, and coaching note your team captures lives in Jiminny. Jiminny MCP gives your agent authenticated access to call intelligence scoped to the user who authorized it.

  • Acts as the user: Access and write actions stay tied to the Jiminny account that authorized the agent.
  • Credentials stay vaulted: AES-256, resolved at request time, never in LLM context.
  • Scoped before every call: User permissions enforced. 90-day audit trail.
Jiminny
agent · Acme Q3
Run
Find calls this week where next steps were unclear and summarize what was agreed.
S
jiminny_search_transcripts
87ms
Sales intelligence agent
3 calls with low next-step clarity. Acme (Oct 29): no date set, agreed to 'follow up soon'. Globex (Oct 28): action vague — 'send something over'. Initech (Oct 27): champion unavailable, no next step logged.
Sources: 3 calls, this week
jiminnymcp
3 calls
18:29
Message Claude...

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_calls_list
List calls
List recorded calls with rep, account, and date range filters.
Parameters
Name
Type
Required
Description
user_email
string
Optional
Filter by rep email
account_name
string
Optional
Filter by account
from_date
string
Optional
ISO 8601 start
to_date
string
Optional
ISO 8601 end
jiminny_call_get
Get call
jiminny_call_transcript
Get transcript
jiminny_call_insights
Get insights
jiminny_search_transcripts
Search transcripts
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
Copied
Show transcripts where budget came up.
Copy the prompt
Copied
Which reps had the most calls today?
Insights & coaching
Copy the prompt
Copied
Get AI insights for [call name].
Copy the prompt
Copied
What was the talk-to-listen ratio for [rep]?
Copy the prompt
Copied
Summarize next steps from [call].
Copy the prompt
Copied
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.
1
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
2
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
3
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
4
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.
SALES
Deal intelligence agent
Combine Gong, Attio, and Slack signals to surface deal risks and next-best actions. Updated after every call.
SALES
Sales call prep agent
Pull Granola notes and Attio contact history to draft a pre-call brief before every sales meeting. Zero rep input.
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.
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.

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
/plugin marketplace add scalekit-inc/claude-code-authstack
/plugin install agentkit@scalekit-auth-stack
Cursor Code REPL
# ~/.cursor/mcp.json
{
""mcpServers"": {
""jiminny"": {
""url"": ""https://mcp.scalekit.com/jiminny"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}
Codex Code REPL
# ~/.codex/config.toml
[mcp_servers.jiminny]
url = ""https://mcp.scalekit.com/jiminny""
auth_env = ""SCALEKIT_TOKEN""
Copilot Code REPL
# .vscode/mcp.json
{
""servers"": {
""jiminny"": {
""url"": ""https://mcp.scalekit.com/jiminny"",
""type"": ""http""
}
}
}