Netlify MCP connector

OAuth 2.0/DCR Developer ToolsAutomationProductivity

Build, deploy, and manage Netlify projects — sites, functions, environment variables, forms, blobs, and edge functions — from AI agents via the Netlify...

Netlify MCP connector

Install the SDK
Section titled “Install the SDK”
- Node.js
- Python
Terminal window
1 npm install @scalekit-sdk/node
Terminal window
1 pip install scalekit
Full SDK reference: Node.js | Python
Set your credentials
Section titled “Set your credentials”

Add your Scalekit credentials to your .env file. Find values in app.scalekit.com > Developers > API Credentials.
.env
```
SCALEKIT_ENVIRONMENT_URL=<your-environment-url>
SCALEKIT_CLIENT_ID=<your-client-id>
SCALEKIT_CLIENT_SECRET=<your-client-secret>
```
Set up the connector
Section titled “Set up the connector”

Register your Netlify MCP credentials with Scalekit so it handles the token lifecycle. You do this once per environment.
Dashboard setup steps

Before connecting Netlify MCP, enable the Model Context Protocol feature in your Slack app settings. This allows your Slack app to share context with LLMs and agents.
1. Enable MCP in your Slack app
  - Go to api.slack.com/apps and open your app.
  - In the left sidebar, under Features, click Agents & AI Apps.
  - Under Model Context Protocol, toggle it on.
2. Connect in Scalekit
  
  In Scalekit dashboard, go to AgentKit > Connections > Create Connection. Find Netlify MCP and click Create.

1
import { ScalekitClient } from '@scalekit-sdk/node'
2
import 'dotenv/config'
3

4
const scalekit = new ScalekitClient(
5
  process.env.SCALEKIT_ENV_URL,
6
  process.env.SCALEKIT_CLIENT_ID,
7
  process.env.SCALEKIT_CLIENT_SECRET,
8
)
9
const actions = scalekit.actions
10

11
const connector = 'netlifymcp'
12
const identifier = 'user_123'
13

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// Generate an authorization link for the user
15
const { link } = await actions.getAuthorizationLink({ connectionName: connector, identifier })
16
console.log('Authorize Netlify MCP:', link)
17
process.stdout.write('Press Enter after authorizing...')
18
await new Promise(r => process.stdin.once('data', r))
19

20
// Make your first call
21
const result = await actions.executeTool({
22
  connector,
23
  identifier,
24
  toolName: 'netlifymcp_get_netlify_coding_context',
25
  toolInput: { creationType: 'YOUR_CREATIONTYPE' },
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})
27
console.log(result)

1
import os
2
from scalekit.client import ScalekitClient
3
from dotenv import load_dotenv
4
load_dotenv()
5

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scalekit_client = ScalekitClient(
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    env_url=os.getenv("SCALEKIT_ENV_URL"),
8
    client_id=os.getenv("SCALEKIT_CLIENT_ID"),
9
    client_secret=os.getenv("SCALEKIT_CLIENT_SECRET"),
10
)
11
actions = scalekit_client.actions
12

13
connection_name = "netlifymcp"
14
identifier = "user_123"
15

16
# Generate an authorization link for the user
17
link_response = actions.get_authorization_link(
18
    connection_name=connection_name,
19
    identifier=identifier,
20
)
21
print("Authorize Netlify MCP:", link_response.link)
22
input("Press Enter after authorizing...")
23

24
# Make your first call
25
result = actions.execute_tool(
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    tool_input={"creationType":"YOUR_CREATIONTYPE"},
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    tool_name="netlifymcp_get_netlify_coding_context",
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    connection_name=connection_name,
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    identifier=identifier,
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)
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print(result)

What you can do

Connect this agent connector to let your agent:

Get netlify coding context — ALWAYS call when writing code
Reader netlify deploy services, netlify extension services, netlify project services — Read Netlify deploy information
Updater netlify deploy services, netlify extension services, netlify project services — Write operations for Netlify deployments

Common workflows

Proxy API call

Node.js
Python

1
import { ScalekitClient } from '@scalekit-sdk/node'
2
import 'dotenv/config'
3

4
const scalekit = new ScalekitClient(
5
  process.env.SCALEKIT_ENV_URL,
6
  process.env.SCALEKIT_CLIENT_ID,
7
  process.env.SCALEKIT_CLIENT_SECRET,
8
)
9
const actions = scalekit.actions
10

11
const connector = 'netlifymcp'
12
const identifier = 'user_123'
13

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// Read a channel's message history
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const result = await actions.executeTool({
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  connector,
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  identifier,
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  toolName: 'netlifymcp_slack_read_channel',
19
  toolInput: { channel_id: 'C01234567' },
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})
21
console.log(result)

1
from scalekit import ScalekitClient
2
import os
3

4
scalekit_client = ScalekitClient(
5
    os.environ['SCALEKIT_ENV_URL'],
6
    os.environ['SCALEKIT_CLIENT_ID'],
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    os.environ['SCALEKIT_CLIENT_SECRET'],
8
)
9

10
connector = 'netlifymcp'
11
identifier = 'user_123'
12

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# Read a channel's message history
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result = scalekit_client.actions.execute_tool(
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    connector=connector,
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    identifier=identifier,
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    tool_name='netlifymcp_slack_read_channel',
18
    tool_input={'channel_id': 'C01234567'},
19
)
20
print(result)

Tool list

Use the exact tool names from the Tool list below when you call execute_tool. If you’re not sure which name to use, list the tools available for the current user first.

netlifymcp_get_netlify_coding_context # ALWAYS call when writing code. Required step before creating or editing any type of Netlify functions, SDK/library usage, etc. Returns up-to-date code patterns and guidance for the selected creation type. 1 param

ALWAYS call when writing code. Required step before creating or editing any type of Netlify functions, SDK/library usage, etc. Returns up-to-date code patterns and guidance for the selected creation type.