Lucid MCP MCP | Oauth 2.0 Lucid MCP connector for AI agents

CALL ANY TOOL

List and get documents, create new diagrams, list shapes on pages, and export as PDF or image.

lucid_documents_list

List documents

List Lucid documents accessible to the user.

Parameters

Name

Type

Required

Description

product

string

Optional

Product filter: lucidchart, lucidspark

limit

integer

Optional

Max documents

lucid_document_get

Get document

lucid_document_create

Create document

lucid_shapes_list

List shapes

lucid_document_export

Export document

Build your Agent

Drop the toolkit in, point it at the user, and your diagramming agent can use Lucid MCP from the first run.

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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: ["lucidmcp"], toolNames: ["lucid_documents_list", "lucid_document_get", "lucid_document_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 });

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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: ["lucidmcp"], toolNames: ["lucid_documents_list", "lucid_document_get", "lucid_document_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: ["lucidmcp"], toolNames: ["lucid_documents_list", "lucid_document_get", "lucid_document_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 }],
});

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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/lucidmcp",
    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 agent to start using Lucid MCP.

Documents & diagrams

Copy the prompt

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List all architecture diagrams.

Copy the prompt

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Get the pages in [document name].

Copy the prompt

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List shapes on page [name] of [document].

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Find documents updated this sprint.

Action & export

Copy the prompt

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Create a new Lucidchart titled [title].

Copy the prompt

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Export [document] as PDF.

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Export [diagram] as PNG.

Copy the prompt

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List all documents shared with [email].

Reporting

Copy the prompt

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Which diagrams have been updated this month?

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List all documents in [project].

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Find diagrams with [keyword] in the title.

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How many pages does [document] have?

SEE HOW AUTH WORKS

Users authorize Lucid MCP once. Their credentials stay vaulted, every call is checked, and every action is logged.

1

Authorize

Your user connects

Lucid 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

Lucid 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

Lucid 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

Lucid 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 diagramming agents and MCP connectors. Working code, live demos, fork what fits.

ENGINEERING

Engineering standup agent

Aggregate GitHub and GitLab activity, link to Jira, and post a daily standup digest to Slack. No async updates.

ENGINEERING

DevOps assistant agent

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

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.

Lucid 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 Lucid MCP 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 Lucid MCP oauth 2.0 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 Lucid MCP?

Yes. Pass a tool name filter to listScopedTools so the diagramming agent only sees the subset you authorize. Pre-API-call scope checks block out-of-policy actions before the request reaches Lucid MCP.

What happens when a user revokes Lucid MCP 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.

Can the agent access documents in team folders the user hasn't been invited to?

No. Document access resolves the authorizing user's Lucid OAuth scope. Team folders and shared documents the user hasn't been given access to are not returned.

Lucid MCP

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