Slite MCP connector for AI agents

Tools your agent reaches for on Slite MCP, scoped per user.

CALL ANY TOOL

Search notes, manage channels, create and update docs, and add comments in Slite. Same toolkit, every framework, no auth plumbing.

slitemcp_append-blocks

Append-Blocks

Append sliteml content blocks to an existing note, optionally anchoring them before or after a specific block.

Parameters

Name

Type

Required

Description

noteId

string

Required

The ID of the note to append blocks to.

sliteml

string

Required

sliteml content blocks to insert. Block IDs are auto-generated and returned in changedContent.

afterBlockId

string

Optional

Block ID to insert after; read from trailing {#blockId} markers in a get-note response.

beforeBlockId

string

Optional

Block ID to insert before; read from trailing {#blockId} markers in a get-note response.

returnFullDocument

boolean

Optional

If true, include the full updated document in the response. Defaults to false.

slitemcp_archive-note

Archive-Note

slitemcp_ask-slite

Ask-Slite

slitemcp_create-channel

Create-Channel

slitemcp_create-collection

Create-Collection

slitemcp_create-comment-thread

Create-Comment-Thread

slitemcp_create-note

Create-Note

slitemcp_get-comment-thread-on-note

Get-Comment-Thread-On-Note

slitemcp_get-note

Get-Note

slitemcp_get-note-children

Get-Note-Children

slitemcp_get-user

Get-User

slitemcp_get-user-group

Get-User-Group

slitemcp_list-channels

List-Channels

slitemcp_list-comment-threads

List-Comment-Threads

slitemcp_list-empty-notes-for-knowledge-management

List-Empty-Notes-For-Knowledge-Management

slitemcp_list-inactive-notes-for-knowledge-management

List-Inactive-Notes-For-Knowledge-Management

slitemcp_list-notes-for-knowledge-management

List-Notes-For-Knowledge-Management

slitemcp_list-public-notes-for-knowledge-management

List-Public-Notes-For-Knowledge-Management

slitemcp_list-recently-edited-notes

List-Recently-Edited-Notes

slitemcp_list-recently-visited-notes

List-Recently-Visited-Notes

slitemcp_modify-block

Modify-Block

slitemcp_modify-range

Modify-Range

slitemcp_move-note

Move-Note

slitemcp_remove-blocks

Remove-Blocks

slitemcp_reply-to-comment-thread

Reply-To-Comment-Thread

slitemcp_resolve-comment-thread

Resolve-Comment-Thread

slitemcp_restore-note

Restore-Note

slitemcp_search-notes

Search-Notes

slitemcp_search-user-groups

Search-User-Groups

slitemcp_search-users

Search-Users

For more tools, view docs.

Build your Agent

Drop the toolkit in, point it at the user, and your agent can search Slite notes and manage workspace docs from the first run.

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: ["slitemcp"], toolNames: ["slite_notes_search", "slite_note_get", "slite_note_create"] },
  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 });

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: ["slitemcp"], toolNames: ["slite_notes_search", "slite_note_get", "slite_note_create"] },
  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: ["slitemcp"], toolNames: ["slite_notes_search", "slite_note_get", "slite_note_create"] },
  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 }],
});

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/slitemcp",
    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 knowledge agent to start searching and creating Slite documentation.

Search & recall

Copy the prompt

Copied

Find all docs about [topic] in the Slite workspace.

Copy the prompt

Copied

Get the content of the [doc name] note.

Copy the prompt

Copied

List all channels in my Slite workspace.

Action & create

Copy the prompt

Copied

Create a new note titled [title] in the [channel] channel.

Copy the prompt

Copied

Update the [doc name] note with [new content].

Copy the prompt

Copied

Find all onboarding docs updated in the last [N] days.

SEE HOW AUTH WORKS

Users authorize Slite once. Their workspace credentials stay vaulted, every note action is scoped to their account, and every call is logged.

Authorize

Your user connects

Slite 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

Slite 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

Slite 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

Slite 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

Same per-user auth pattern across other collaboration and file management connectors.

ENGINEERING

Auto-release notes agent

Group merged GitHub PRs by feature, fix, or chore and publish release notes per tag. No manual changelog grooming.

GitHub

View agent

ENGINEERING

DevOps assistant agent

Triage GitHub incidents, open Linear tickets, and notify the on-call channel in Slack with context already attached.

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 user identity, scope enforcement, and audit.

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 so attribution, audit, and scope stay accurate.

// 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.

Slite MCP 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 Slite as the user or a shared account?

As the user. Each workspace member authorizes once and notes created by the agent appear under that user's identity in Slite.

Where is the Slite OAuth token stored?

In Scalekit's AES-256 vault, namespaced per tenant. Tokens never appear in prompts or LLM context.

Can I restrict the agent to read-only note access?

Yes. Use listScopedTools to allow note search and retrieval without granting creation or update permissions.

What happens when a user revokes Slite access?

The connection is invalidated on the next tool call. Subsequent requests fail closed. Other workspace members remain unaffected.

Can the agent create Slite notes from meeting transcripts in one workflow?

Yes. A single agent can pull Fireflies or OtterAI transcripts and create Slite notes in the same workflow, using the same user identity.

On this page

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