Attention MCP | API key connector for AI agents

Tools your call intelligence agent reaches for on Attention, scoped per user.

CALL ANY TOOL

Search calls and transcripts, surface insights, and pull AI summaries from sales conversations.

attention_calls_list

List calls

List recorded calls with optional filters for rep, account, and date range.

Parameters

Name

Type

Required

Description

rep_email

string

Optional

Filter by sales rep email

account_id

string

Optional

Filter by account

start_date

string

Optional

ISO 8601 start

end_date

string

Optional

ISO 8601 end

attention_call_get

Get call

attention_call_transcript

Get transcript

attention_call_summary

Get summary

attention_insights_search

Search insights

Build your Agent

Drop the toolkit in, point it at the user, and your call intelligence agent can use Attention 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: ["attention"], toolNames: ["attention_calls_list", "attention_call_get", "attention_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 });

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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: ["attention"], toolNames: ["attention_calls_list", "attention_call_get", "attention_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: ["attention"], toolNames: ["attention_calls_list", "attention_call_get", "attention_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 }],
});

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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/attention",
    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 Attention.

Search & recall

Copy the prompt

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Find all calls mentioning [competitor name].

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List calls with [account name] this month.

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Show me transcripts where [objection] came up.

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Which deals had risk flags this week?

Insights & summaries

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Summarize the last 5 calls with [account].

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Get action items from [call name].

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What objections appeared in calls this week?

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Show coaching opportunities for [rep name].

Reporting & coaching

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Compare win rates by objection type.

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List reps with most competitor mentions.

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Which calls scored highest on next-step clarity?

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Show all calls where pricing came up.

SEE HOW AUTH WORKS

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

Authorize

Your user connects

Attention

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

Attention

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

Attention

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

Attention

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 call 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.

Granola

Attio

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.

Call insights attributed to a bot, not the rep

A shared Attention API key looks fine in a demo. In production, every call insight and coaching signal retrieved looks like it came from a service account. Rep-level attribution breaks. Scalekit resolves the actual user's token so every Attention action is attributed correctly.

// 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.

Attention today. Gong, Chorus, Fathom 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 Attention 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 Attention api key stored?

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

What happens when a user revokes Attention access?

Which calls can the agent surface insights for?

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"": {
""attention"": {
""url"": ""https://mcp.scalekit.com/attention"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}

Codex Code REPL

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

Copilot Code REPL

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