ListenLabs MCP

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OAUTH 2.0

ANALYTICS

Analytics

Listen Labs is a qualitative research platform for creating, launching, and analyzing studies with AI assistance. This MCP connector gives AI agents...

  • Acts as the user: Every tool call runs as the authorizing user. Access and audit trail stay intact.
  • Credentials stay vaulted: AES-256 encrypted, resolved at request time, never stored in LLM context.
  • Scoped before every call: Per-user permissions enforced automatically. 90-day audit trail included.
ListenLabs MCP
agent · Acme Q3
Run
Create Study in ListenLabs MCP
S
listenlabsmcp_create_study
85ms
ListenLabs MCP agent
Start a new guided user-interview study. provide a plain-language description of the study goals and target audience. th.
Sources: ListenLabs MCP
listenlabsmcpmcp
1 tool call
18:29
Message Claude...

Tools your agent reaches for on Listen Labs, scoped per user.

CALL ANY TOOL
Create, launch, and analyze qualitative research studies inside the user's workspace.
listenlabsmcp_create_study
Start a new guided user-interview study. provide a plain-lan
Start a new guided user-interview study. provide a plain-language description of the study goals and target audience. the platform's creation agent walks through onboarding stages; subsequent turns must use edit_study with the returned studyid and chatid.
Parameters
Name
Type
Required
Description
prompt
string
Required
Plain-language description of the study to create (goals, who to interview, what to learn)
orgId
string
Optional
Organization UUID when known (e.g. from list_creatable_orgs)
orgName
string
Optional
Organization name to create the study in. Required when the user belongs to multiple organizations.
listenlabsmcp_edit_study
Send a natural-language edit instruction or structured butto
listenlabsmcp_get_response
Deep-dive into a single respondent's interview. returns a st
listenlabsmcp_get_study_analysis
Get the ai-generated analysis report for a study, rendered a
listenlabsmcp_get_study_responses
Get response transcripts for a study. returns formatted inte
listenlabsmcp_get_study_state
Return the current state of a study — title, audience, study
listenlabsmcp_launch_study
Publish the study's draft revision (if needed) and start all
listenlabsmcp_list_creatable_orgs
List organizations the user belongs to where they can create
listenlabsmcp_list_studies
List studies accessible to the authenticated user. returns s
listenlabsmcp_publish_study
Publish the study's current draft revision so respondents se
listenlabsmcp_search_across_studies
Search across study metadata using a text query. returns mat
Build your Agent
Same auth pattern across LangChain, OpenAI, Anthropic, and Google ADK.
Python · LlamaIndex
from langchain_mcp_adapters.client import MultiServerMCPClient
from scalekit import ScalekitClient

client = ScalekitClient(env_url=ENV_URL, client_id=CLIENT_ID, client_secret=SECRET)
token = client.agent.get_token(user_id="user_id", connector="listenlabsmcp")

mcp = MultiServerMCPClient({
"listenlabsmcp": {
"url": "https://mcp.scalekit.com/listenlabsmcp",
"headers": {"Authorization": "Bearer " + token}
}
})
tools = await mcp.get_tools()
import OpenAI from "openai";
import { ScalekitClient } from "@scalekit-sdk/node";

const client = new ScalekitClient({ envUrl, clientId, clientSecret });
const token = await client.agent.getToken({ userId: "user_id", connector: "listenlabsmcp" });

const openai = new OpenAI();
// Connect to MCP at https://mcp.scalekit.com/listenlabsmcp
// Pass: Authorization: Bearer + token
import Anthropic from "@anthropic-ai/sdk";
import { ScalekitClient } from "@scalekit-sdk/node";

const client = new ScalekitClient({ envUrl, clientId, clientSecret });
const token = await client.agent.getToken({ userId: "user_id", connector: "listenlabsmcp" });

const anthropic = new Anthropic();
// Connect to MCP at https://mcp.scalekit.com/listenlabsmcp
// Pass: Authorization: Bearer + token
from google.adk.agents import LlmAgent
from scalekit import ScalekitClient

client = ScalekitClient(env_url=ENV_URL, client_id=CLIENT_ID, client_secret=SECRET)
token = client.agent.get_token(user_id="user_id", connector="listenlabsmcp")
# Connect to MCP at https://mcp.scalekit.com/listenlabsmcp
# Pass: Authorization: Bearer + token
Try these prompts
Copy any prompt into your agent. Each maps directly to a ListenLabs tool. Click to copy, paste into your agent, done.
Get started
Copy the prompt
Copied
Search across study metadata using a text query?
Copy the prompt
Copied
Publish the study’s current draft revision so respondents see the latest version?
Advanced
Copy the prompt
Copied
List studies accessible to the authenticated user?
Copy the prompt
Copied
Return the current state of a study — title, audience, study guide, questions, screener, and recruitment details?
SEE HOW AUTH WORKS
Your users connect once. Their ListenLabs credentials stay vaulted, every call is scope-checked, and every action is logged.
1
Authorize
Your user connects
ListenLabs 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
ListenLabs 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
ListenLabs 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
ListenLabs 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 connectors.
SALES
Deal intelligence agent
Combine Gong, Attio, and Slack signals to surface deal risks and next-best actions. Updated after every call.
ENGINEERING
Engineering standup agent
Aggregate GitHub and GitLab activity, link to Jira, and post a daily standup digest to Slack. No async updates.
Why Scalekit
Secure your agent's access. Connectors ship in minutes
Other connector libraries treat auth as a demo afterthought. Scalekit starts with identity, scope enforcement, and audit. Connectors follow.
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.
// shared token
audit → bot_service_account

// scalekit
audit → user_abc ✓
02.
Authentication is not authorization
03.
Multi-tenancy is architectural
04.
One connector today. Ten 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 ListenLabs 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 ListenLabs OAuth token 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 ListenLabs?
Yes. Pass a tool name filter to listScopedTools so the analytics agent only sees the subset you authorize. Pre-API-call scope checks block out-of-policy actions before the request reaches ListenLabs.

What happens when a user revokes ListenLabs 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.

Who can read study responses and analysis?
Only the authorizing user's Listen Labs workspace. Study creation, responses, and AI analysis stay inside that research team's account per call.

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"": {
""listenlabsmcp"": {
""url"": ""https://mcp.scalekit.com/listenlabsmcp"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}
Codex Code REPL
# ~/.codex/config.toml
[mcp_servers.listenlabsmcp]
url = ""https://mcp.scalekit.com/listenlabsmcp""
auth_env = ""SCALEKIT_TOKEN""
Copilot Code REPL
# .vscode/mcp.json
{
""servers"": {
""listenlabsmcp"": {
""url"": ""https://mcp.scalekit.com/listenlabsmcp"",
""type"": ""http""
}
}
}