Dovetail connector for AI agents

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

CALL ANY TOOL

Search insights and highlights, read projects and notes, and capture new findings, each call scoped to the authenticated researcher's workspace access.

dovetailmcp_create_channel_datum

Create channel datum

Send a new data point to a Dovetail channel for automated AI processing.

Parameters

Name

Type

Required

Description

channel_id

string

Required

The unique identifier of the Dovetail channel to which the data point will be sent.

content

string

Required

The raw text content of the data point to send (e.g. an app review, NPS response, or support ticket body).

title

string

Optional

Optional title or short label for the data point. Helps identify the item in the channel's data list.

dovetailmcp_create_comment

Create comment

dovetailmcp_create_doc

Create doc

dovetailmcp_create_folder

Create folder

dovetailmcp_create_tag

Create tag

dovetailmcp_download_file

Download file

dovetailmcp_get_channel

Get channel

dovetailmcp_get_channel_datum

Get channel datum

dovetailmcp_get_data_content

Get data content

dovetailmcp_get_doc

Get doc

dovetailmcp_get_doc_content

Get doc content

dovetailmcp_get_dovetail_projects

Get dovetail projects

dovetailmcp_get_file

Get file

dovetailmcp_get_folder

Get folder

dovetailmcp_get_highlight

Get highlight

dovetailmcp_get_insight_content

Get insight content

dovetailmcp_get_project_data

Get project data

dovetailmcp_get_project_highlights

Get project highlights

dovetailmcp_get_tag

Get tag

dovetailmcp_list_channel_data

List channel data

dovetailmcp_list_channel_themes

List channel themes

dovetailmcp_list_channels

List channels

dovetailmcp_list_doc_comments

List doc comments

dovetailmcp_list_docs

List docs

dovetailmcp_list_folders

List folders

dovetailmcp_list_personal_docs

List personal docs

dovetailmcp_list_project_data

List project data

dovetailmcp_list_project_templates

List project templates

dovetailmcp_list_tags

List tags

dovetailmcp_search_workspace

Search workspace

For more tools, view docs.

Build your Agent

The same per-user auth pattern wires into every agent framework.

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

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

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["dovetailmcp"], toolNames: ["dovetail_insights_search", "dovetail_insight_get", "dovetail_highlights_list"] },
  pageSize: 100,
});

const agent = createReactAgent({ llm: new ChatOpenAI({ model: "gpt-4o" }), tools });
await agent.invoke({ messages: [{ role: "user", content: "Summarise onboarding friction insights" }] });

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

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

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["dovetailmcp"], toolNames: ["dovetail_insights_search", "dovetail_insight_get", "dovetail_highlights_list"] },
  pageSize: 100,
});

const res = await openai.responses.create({
  model: "gpt-4o",
  input: "Search insights about onboarding friction",
  tools,
});

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

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

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["dovetailmcp"], toolNames: ["dovetail_insights_search", "dovetail_insight_get", "dovetail_highlights_list"] },
  pageSize: 100,
});

const msg = await anthropic.messages.create({
  model: "claude-sonnet-4-6",
  max_tokens: 1024,
  tools,
  messages: [{ role: "user", content: "Pull highlights linked to the pricing insight" }],
});

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

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

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["dovetailmcp"], toolNames: ["dovetail_insights_search", "dovetail_insight_get", "dovetail_highlights_list"] },
  pageSize: 100,
});

const agent = new LlmAgent({ name: "dovetail_agent", model: "gemini-2.0-flash", tools });
await agent.run("List insights in the onboarding project");

Try these prompts

Paste any prompt into your agent to start working Dovetail research.

Search & synthesize

Copy the prompt

Copied

What are customers saying about onboarding friction this quarter?

Copy the prompt

Copied

Find insights tagged pricing across all my projects.

Copy the prompt

Copied

Summarise the top themes in the Q2 discovery project.

Investigate

Copy the prompt

Copied

Pull the highlights linked to the checkout confusion insight.

Copy the prompt

Copied

Show me raw transcript data that mentions slow first-value.

Copy the prompt

Copied

Which project has the most highlights about mobile bugs?

Capture & recall

Copy the prompt

Copied

Create a note in the onboarding project summarising this week's calls.

Copy the prompt

Copied

List all research projects I have access to.

Copy the prompt

Copied

How many insights are published in the retention study?

SEE HOW AUTH WORKS

The researcher authorises Dovetail once. Every later agent call uses their token with project scope enforced.

Authorize

Your user connects

Dovetail 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

Store

Their

Dovetail 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

Resolve

When your agent calls a

Dovetail 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

Audit

Every

Dovetail 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 analytics connectors.

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.

Support and Ops Teams

Freshdesk CSAT agent

Watches Freshdesk for resolved tickets, emails each requester a CSAT survey from Gmail, and writes the score and the verbatim back onto the ticket, every call on the support rep's own delegated OAuth.

Freshdesk

Gmail

View agent

Support and Ops Teams

Support ticket automation agent

Fetches new Zendesk tickets, drafts a reply from Notion knowledge base articles, digests what it cannot answer to Slack, and archives the rest, acting as the support agent rather than a shared API key.

Support and Ops Teams

Meeting prep

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

Why Scalekit

Secure your agent's access. Connectors ship in minutes

01.

Shared tokens break per-user analytics

A shared Dovetail token looks fine in a demo. In production every note and edit logs as one service account, so you lose track of which researcher acted. Scalekit resolves the real user's credential per call.

// shared token
create_note → research_bot

// scalekit
create_note → user_abc ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

Dovetail 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 Dovetail as the user or as a shared key?

As the user. Scalekit resolves the authenticated researcher's own Dovetail credential at request time, so every insight read, note, and edit is attributed to them in the workspace rather than a shared research bot.

Where is the Dovetail token stored?

In an AES-256 encrypted vault, namespaced per tenant. Tokens are fetched only at the moment of a tool call, refreshed automatically before expiry, and revocable from a single dashboard, never exposed to the model or the prompt.

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

Yes. Pass a toolNames filter to listScopedTools to expose only the actions you want, for example read-only insight and highlight search without note creation. Scalekit also runs a scope check before each API call.

What happens when a user revokes Dovetail access?

The credential is invalidated at the next tool call, which fails closed rather than falling back to a shared token. Other users are unaffected, and the revocation is recorded in the audit trail.

Can the agent search raw transcripts, or only published insights?

That is your choice. Expose dovetail_insights_search and dovetail_highlights_list for a synthesis agent that reads finished insights, or add dovetail_data_search to let it query raw transcripts, tickets, and call notes. Each call is scoped per user, so a researcher can only reach data in projects they already own.

On this page

Tool and actions Framework Compatibility Starter Prompt Auth Lifecycle Try Other Agents Why Scalekit