Microsoft Excel MCP for AI agents

CALL ANY TOOL

List workbooks and worksheets, read and write cell ranges, get named tables, and retrieve chart metadata.

excel_workbooks_list

List workbooks

List Excel workbooks in the user's OneDrive or SharePoint.

Parameters

Name

Type

Required

Description

limit

integer

Optional

Max workbooks

excel_worksheets_list

List worksheets

excel_range_get

Get range

excel_range_update

Update range

excel_table_get

Get table

excel_chart_get

Get chart

Build your Agent

Drop the toolkit in, point it at the user, and your data agent can use Microsoft Excel 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: ["microsoftexcel"], toolNames: ["excel_workbooks_list", "excel_worksheets_list", "excel_range_get"] },
  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: ["microsoftexcel"], toolNames: ["excel_workbooks_list", "excel_worksheets_list", "excel_range_get"] },
  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: ["microsoftexcel"], toolNames: ["excel_workbooks_list", "excel_worksheets_list", "excel_range_get"] },
  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/microsoftexcel",
    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 Microsoft Excel.

Read & analyze

Copy the prompt

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Read [range] from [sheet name] in [workbook].

Copy the prompt

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Get all rows in [table name].

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List all worksheets in [workbook].

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Which rows in [sheet] have [column] empty?

Write & update

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Update cell [A1] in [sheet] to [value].

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Write [data] to range [A1:D10].

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Append a row to [table]: [values].

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Clear range [A2:Z100] in [sheet].

Reporting

Copy the prompt

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Read the Q4 budget workbook and flag over-budget departments.

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Sum column [name] in [sheet].

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List workbooks updated this week.

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Get chart image from [sheet].

SEE HOW AUTH WORKS

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

1

Authorize

Your user connects

Microsoft Excel

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

Microsoft Excel

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

Microsoft Excel

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

Microsoft Excel

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

GTM

CRM AI agent

Log calls, update opportunity stages, and surface stalled deals across HubSpot or Salesforce. No manual data entry.

HubSpot

Salesforce

View agent

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.

Microsoft Excel 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 Microsoft Excel 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 Microsoft Excel 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 Microsoft Excel?

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

What happens when a user revokes Microsoft Excel 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 write to workbooks stored in shared SharePoint sites the user has view-only access to?

No. Write operations require the authorizing user to have edit permissions on the file. View-only access blocks all range updates. The agent inherits the exact file rights the user has in OneDrive or SharePoint.

Microsoft Excel

Tools your data agent reaches for on Microsoft Excel, scoped per user.