Perspective AI MCP

Live

OAUTH 2.1

AI INTERVIEWS

Automation

Perspective AI MCP gives your agent AI interviews: create perspectives, invite participants, search and analyze conversations, read insights, and automate follow-ups.

  • Per-user credentials: each call uses the actual user's token, never a shared bot.
  • Encrypted per-tenant vault: AES-256, resolved at request time, never in LLM context.
  • Scoped before every call: pre-call scope check, 90-day SIEM-exportable audit chain.
Perspective AI MCP
agent · Acme Q3
Run
What did churned customers say about pricing in this month's exit interviews?
S
perspectiveaimcp_conversations_search
164ms
Research agent
Pricing came up in 7 of 18 exit conversations. Most said the jump from the starter plan was too steep; 3 named a cheaper competitor.
Sources: Exit interview perspective, 18 conversations
perspectiveaimcp
18 conversations
18:29
Message Claude...

Tools your interview research agent reaches for on Perspective AI, scoped per user.

CALL ANY TOOL
35 tools for AI interviews: design perspectives, invite participants, search and analyze conversations, read insights and stats, and manage automations.
perspectiveaimcp_agent_template_get
Get agent template
Returns the full detail of one agent template from the public Agent Library: what it captures, how the conversation runs, the integrations it typically feeds, where it sits in the library, and related templates. Behavior: - Read-only, public catalog, no workspace is needed. - Unknown slugs return an error; slugs come from agent_template_search. When to use this tool: - You picked an agent template with agent_template_search and want enough detail to describe it to the user or tailor the perspective_create brief. When NOT to use this tool: - You don't have a slug yet, use agent_template_search.
Parameters
Name
Type
Required
Description
slug
string
Required
Agent template slug from agent_template_search.
perspectiveaimcp_agent_template_search
Search agent template
perspectiveaimcp_automation_create
Create automation
perspectiveaimcp_automation_delete
Delete automation
perspectiveaimcp_automation_list
List automation
perspectiveaimcp_automation_test
Automation test
perspectiveaimcp_automation_update
Update automation
perspectiveaimcp_conversations_data_analysis
Conversations data analysis
perspectiveaimcp_conversations_explorer
Conversations explorer
perspectiveaimcp_conversations_search
Search conversations
perspectiveaimcp_participant_invite
Invite participant
perspectiveaimcp_perspective_await_job
Perspective await job
perspectiveaimcp_perspective_create
Create perspective
perspectiveaimcp_perspective_get
Get perspective
perspectiveaimcp_perspective_get_conversation
Perspective get conversation
perspectiveaimcp_perspective_get_conversations
Perspective get conversations
perspectiveaimcp_perspective_get_embed_options
Perspective get embed options
perspectiveaimcp_perspective_get_preview_link
Perspective get preview link
perspectiveaimcp_perspective_get_stats
Perspective get stats
perspectiveaimcp_perspective_import_conversation
Perspective import conversation
perspectiveaimcp_perspective_list
List perspective
perspectiveaimcp_perspective_list_conversations
Perspective list conversations
perspectiveaimcp_perspective_update
Update perspective
perspectiveaimcp_read_insight
Read insight
perspectiveaimcp_read_insights
Read insights
perspectiveaimcp_read_perspective_status
Read perspective status
perspectiveaimcp_slack_channel_refresh
Refresh slack channel
perspectiveaimcp_slack_channel_refresh_status
Slack channel refresh status
perspectiveaimcp_slack_channel_search
Search slack channel
perspectiveaimcp_workspace_get
Get workspace

For more tools, view docs.

Build your Agent
Same auth pattern across LangChain, OpenAI, Anthropic, and Google ADK.
Python · LlamaIndex
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);

// Perspective AI MCP tools scoped to this user
const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["perspectiveaimcp"], toolNames: [
    "perspectiveaimcp_perspective_list",
    "perspectiveaimcp_conversations_search",
    "perspectiveaimcp_read_insights"] },
  pageSize: 100,
});

const agent = createReactAgent({ llm, tools });
await agent.invoke({ messages: [{ role: "user", content: "Summarize what churned customers said about pricing" }] });
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: ["perspectiveaimcp"] }, pageSize: 100,
});

const res = await openai.chat.completions.create({
  model: "gpt-5",
  messages: [{ role: "user", content: "Summarize what churned customers said about pricing" }],
  tools,
});

// Execute the tool call with the user's vaulted Perspective AI 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: ["perspectiveaimcp"] }, pageSize: 100,
});

const msg = await anthropic.messages.create({
  model: "claude-sonnet-5",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Summarize what churned customers said about pricing" }],
  tools,
});

// Tool call runs with the user's vaulted Perspective AI 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: ["perspectiveaimcp"] }, pageSize: 100,
});

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

await agent.run("Summarize what churned customers said about pricing");
Try these prompts
Copy any prompt into your agent. Each maps directly to a Perspective AI MCP tool. Click to copy, paste into your agent, done.
Design and launch
Copy the prompt
Copied
Create a perspective that interviews trial users about onboarding friction.
Copy the prompt
Copied
Make the outline shorter and add a budget question.
Copy the prompt
Copied
Invite these 10 customers to the onboarding interview.
Conversations
Copy the prompt
Copied
What did churned customers say about pricing this month?
Copy the prompt
Copied
Show completion rate and average duration for this perspective over 30 days.
Copy the prompt
Copied
Import this Zoom call transcript into the discovery perspective.
Insights and automations
Copy the prompt
Copied
List the latest insights for the onboarding perspective.
Copy the prompt
Copied
Send a weekly digest of new conversations to #research in Slack.
Copy the prompt
Copied
Which automations on this perspective failed recently?
SEE HOW AUTH WORKS
Each user signs in to Perspective AI once; Scalekit stores and refreshes their tokens. Tokens stay vaulted, every call is scope checked, and every action is logged.
1
Authorize
Your user connects
Perspective AI 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
Perspective AI 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
Perspective AI 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
Perspective AI 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 research and automation 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.
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.
People Ops and HR teams
Performance review collector
Collects review feedback from Airtable and Google Forms scoped to each manager's direct reports, writes per-employee summaries to Notion, and DMs the manager a Slack digest.
Engineering Teams
Slack triage
Polls Slack for new messages, classifies bugs and support requests with a LangGraph router, files GitHub issues or Zendesk tickets, and confirms in the thread.
Test other agents
See the same per-user auth pattern across other research and automation 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.
SALES
Deal intelligence agent
Score Gong call risk with an LLM, cross-reference the Attio record, and DM each owner their at-risk deals in Slack.
PEOPLE OPS
Performance review collector agent
Collect review feedback from Airtable and Google Forms per manager, summarise each report in Notion, and DM the digest in Slack.
ENGINEERING
Slack triage agent
Classify new Slack messages as bugs or support requests, file the GitHub issue or Zendesk ticket, and reply in the thread.
Why Scalekit
Secure your agent's access. Connectors ship in minutes
01.
Shared tokens break per-user analytics
A shared Perspective AI token looks fine in a demo. In production every interview and conversation read 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.
Perspective AI 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 Perspective AI as the user or as a shared key?
As the user. Each user signs in to Perspective AI once, and Scalekit stores and refreshes their tokens. Audit logs attribute every interview and conversation read to that user, not a shared service account.
Where is the Perspective AI 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 Perspective AI?
Yes. Filter by tool name in listScopedTools to expose only what you want. Scalekit also enforces scope checks before every API call.
What happens when a user revokes Perspective AI 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.
How does the agent analyze Perspective AI conversations?
Three ways: conversations_search returns a synthesized answer with source references, conversations_explorer runs a deeper text exploration with citations, and conversations_data_analysis runs aggregation queries for counts and trends. Each runs within the perspectives the user can access.
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"": {
""perspectiveaimcp"": {
""url"": ""https://mcp.scalekit.com/perspectiveaimcp"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}
Codex Code REPL
# ~/.codex/config.toml
[mcp_servers.perspectiveaimcp]
url = ""https://mcp.scalekit.com/perspectiveaimcp""
auth_env = ""SCALEKIT_TOKEN""
Copilot Code REPL
# .vscode/mcp.json
{
""servers"": {
""perspectiveaimcp"": {
""url"": ""https://mcp.scalekit.com/perspectiveaimcp"",
""type"": ""http""
}
}
}