Arcmira MCP connector for AI agents

CALL ANY TOOL

4 tools for podcast and YouTube research in Arcmira: read the client reference, query transcripts, mentions, sponsors, and monitors, create and update monitors, and send feedback to the Arcmira team.

arcmiramcp_arcmira_describe

Arcmira describe

Returns the reference for the typed arcmira client the execute tools run: method arguments, return fields, entity ID rules, examples, errors, the budget and monitor rules, and documentation links. The optional topic narrows it to a method or subject. Reads no indexed content and bills nothing. Docs: https://arcmira.com/docs/mcp-server

Parameters

Name

Type

Required

Description

intent

string

Optional

Optional: the user's request in a few words. Logged with this call to improve Arcmira.

topic

string

Optional

One word to narrow the reference, like sponsors, resolve, transcript, monitors or dates. Omit for the whole reference (about 25,000 characters).

arcmiramcp_arcmira_execute_read

Read arcmira execute

arcmiramcp_arcmira_execute_write

Write arcmira execute

arcmiramcp_arcmira_feedback

Arcmira feedback

Build your Agent

Same auth pattern across LangChain, OpenAI, Anthropic, and Google ADK.

import { ScalekitClient } from "@scalekit-sdk/node";
import { createReactAgent } from "@langchain/langgraph/prebuilt";

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);

// Arcmira tools scoped to this user
const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["arcmiramcp"], toolNames: [
    "arcmiramcp_arcmira_describe",
    "arcmiramcp_arcmira_execute_read",
    "arcmiramcp_arcmira_execute_write"] },
  pageSize: 100,
});

const agent = createReactAgent({ llm, tools });
await agent.invoke({ messages: [{ role: "user", content: "Find podcast episodes that mentioned our brand this month and set up a monitor to track it" }] });

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

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);
const openai = new OpenAI();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["arcmiramcp"] }, pageSize: 100,
});

const res = await openai.chat.completions.create({
  model: "gpt-5",
  messages: [{ role: "user", content: "Find podcast episodes that mentioned our brand this month and set up a monitor to track it" }],
  tools,
});

// Execute the tool call with the user's vaulted Arcmira OAuth token
await sk.tools.executeTool(res.choices[0].message.tool_calls[0], "user_123");

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

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);
const anthropic = new Anthropic();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["arcmiramcp"] }, pageSize: 100,
});

const msg = await anthropic.messages.create({
  model: "claude-sonnet-5",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Find podcast episodes that mentioned our brand this month and set up a monitor to track it" }],
  tools,
});

// Tool call runs with the user's vaulted Arcmira OAuth token
await sk.tools.executeTool(msg.content, "user_123");

import { Agent } from "@google/adk/agents";
import { ScalekitClient } from "@scalekit-sdk/node";

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["arcmiramcp"] }, pageSize: 100,
});

const agent = new Agent({
  name: "podcast_research_agent",
  model: "gemini-2.5-pro",
  instruction: "Arcmira tools scoped to this user",
  tools,
});

await agent.run("Find podcast episodes that mentioned our brand this month and set up a monitor to track it");

Try these prompts

Copy any prompt into your agent. Each maps directly to a Arcmira MCP tool. Click to copy, paste into your agent, done.

Transcripts and mentions

Copy the prompt

Copied

Find every podcast episode that mentioned our company in the last 30 days.

Copy the prompt

Copied

Pull the transcript of this YouTube video and summarize the section on pricing.

Copy the prompt

Copied

Which channels cover our category most often?

Sponsors and recommendations

Copy the prompt

Copied

List the sponsors that appeared on this podcast in recent episodes.

Copy the prompt

Copied

Which products does this creator recommend most?

Copy the prompt

Copied

Show how often this competitor appears in sponsor reads.

Monitors and reference

Copy the prompt

Copied

List my Arcmira monitors and what each one tracks.

Copy the prompt

Copied

Create a monitor for mentions of our brand name and add the product name as a second name.

Copy the prompt

Copied

Show me the Arcmira client reference for monitor methods.

SEE HOW AUTH WORKS

Each user signs in to Arcmira once, and Scalekit stores and refreshes their tokens. Credentials stay vaulted, every call is scope checked, and every action is logged.

1

Authorize

Your user connects

Arcmira MCP

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

Arcmira MCP

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

Arcmira MCP

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

Arcmira MCP

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

See the same per-user auth pattern across other search and media connectors.

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.

Support and Ops Teams

Meeting prep

Pulls agenda, participant context, and open action items before every meeting.

Test other agents

See the same per-user auth pattern across other search and media connectors.

GTM

Competitive intelligence briefing agent

Scan Gong calls for competitor mentions, match each one to its Notion battlecard, and DM every affected rep a single Slack digest.

OPS

Meeting prep agent

Assemble the agenda, HubSpot attendee history, and open action items from Gmail before every meeting on the calendar.

Why Scalekit

Secure your agent's access. Connectors ship in minutes

01.

Shared tokens break per-user analytics

A shared Arcmira token looks fine in a demo. In production every transcript query and monitor change looks like one service account, and you cannot tell which user triggered it. Scalekit resolves the credential of the actual user who triggered the agent, never a shared bot.

// shared token
audit → bot_service_account

// scalekit
audit → user_abc ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

Arcmira today. Ten connectors 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 Arcmira as the user or through a shared service account?

As the user. Each user signs in to Arcmira once, and Scalekit stores and refreshes their tokens. Every transcript query and monitor change in your audit trail is attributed to that user, not a shared service account.

Where is the Arcmira OAuth token stored?

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

Can I limit what the agent does in Arcmira?

Yes. Filter by tool name in listScopedTools to expose only what you want, for example arcmiramcp_arcmira_describe and arcmiramcp_arcmira_execute_read without arcmiramcp_arcmira_execute_write. The write tool only creates and updates monitors, and Arcmira documents that nothing is deleted. Scalekit also enforces scope checks before every API call.

What happens when a user revokes Arcmira 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.

Does reading a transcript cost anything?

It can. A Premium read of an untranscribed video starts a transcription that uses credits from the account's plan, then its on-demand budget, and the result reports its charge. The arcmiramcp_arcmira_describe tool reads no indexed content and bills nothing. Both execute tools run for up to 30 seconds and 40 API calls per run.

Arcmira MCP server for AI agents

Tools your podcast research agent reaches for on Arcmira, scoped per user.