Custify MCP Server

Custify MCP Server

Connects AI assistants to Custify customer success data for querying account metrics, health scores, and usage trends. It also supports taking actions such as creating notes, managing tasks, and triggering playbooks.

Category
Visit Server

README

@custify/mcp-server

npm version License: MIT MCP Compatible GitHub Stars

Connect AI tools to your Custify customer success data via the Model Context Protocol.

Query accounts, health scores, usage data, and more — or create notes, tasks, and trigger playbooks — all from within Claude, Cursor, VS Code, or any MCP-compatible AI tool.


Quick Start

Get up and running in under 2 minutes.

1. Get your API key

Go to Custify Settings > Developer > API Access and create or copy your API key.

2. Install

npx @custify/mcp-server

3. Configure your AI tool

See the Configuration section below for your specific tool.

4. Try it out

Ask your AI assistant:

How many churned accounts do I have?

Configuration

<details> <summary><strong>Claude Desktop (STDIO)</strong></summary>

Add the following to your claude_desktop_config.json:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "custify": {
      "command": "npx",
      "args": ["-y", "@custify/mcp-server"],
      "env": {
        "CUSTIFY_API_KEY": "your-api-key-here"
      }
    }
  }
}

Restart Claude Desktop after saving.

</details>

<details> <summary><strong>Cursor (STDIO)</strong></summary>

  1. Open Settings > MCP
  2. Click Add new MCP server
  3. Use the following configuration:
  • Name: custify
  • Command: npx -y @custify/mcp-server
  • Environment Variables: CUSTIFY_API_KEY=your-api-key-here

</details>

<details> <summary><strong>VS Code (STDIO)</strong></summary>

Add to your .vscode/mcp.json in your workspace root:

{
  "servers": {
    "custify": {
      "command": "npx",
      "args": ["-y", "@custify/mcp-server"],
      "env": {
        "CUSTIFY_API_KEY": "your-api-key-here"
      }
    }
  }
}

</details>

<details> <summary><strong>Claude Code (CLI)</strong></summary>

claude mcp add custify -- npx -y @custify/mcp-server \
  --env CUSTIFY_API_KEY=your-api-key-here

</details>

<details> <summary><strong>ChatGPT (Streamable HTTP)</strong></summary>

ChatGPT requires an HTTP-based MCP server. Deploy with Docker:

docker run -d \
  -p 3000:3000 \
  -e CUSTIFY_API_KEY=your-api-key-here \
  ghcr.io/custifyofficial/custify-mcp:latest

Then configure ChatGPT to connect to your server's URL:

https://your-server.example.com/mcp

</details>

<details> <summary><strong>Other MCP Clients</strong></summary>

The Custify MCP server supports two transports:

  • STDIO (default): Run npx @custify/mcp-server with the CUSTIFY_API_KEY environment variable set.
  • Streamable HTTP: Set MCP_TRANSPORT=streamable-http and the server will listen on port 3000 (configurable via PORT). The MCP endpoint is /mcp.

Refer to your MCP client's documentation for how to configure an MCP server using either transport.

</details>


Available Tools

Account Tools

Tool Type Description
list_accounts Read List and filter accounts using Custify's filter system
get_account Read Get full details for a specific account by ID
search_accounts Read Search accounts by name or domain
list_attributes Read Discover all available fields and their types for filtering

list_accounts supports Custify's full filter system. Each filter is an object with fieldName, fieldType, filterType, and filterValue. Use list_attributes first to discover available fields.

Filter examples:

What you want Filter object
Churned accounts {"fieldName": "churned", "fieldType": "Boolean", "filterType": "true"}
Name contains "acme" {"fieldName": "name", "fieldType": "String", "filterType": "contains", "filterValue": "acme"}
Health score > 50 {"fieldName": "metrics.health_scores.<score_id>", "fieldType": "Number", "filterType": "greater", "filterValue": "50"}
Signed up after a date {"fieldName": "signed_up_at", "fieldType": "Date", "filterType": "after", "filterValue": "2024-01-01"}
In a specific segment {"fieldName": "...", "fieldType": "Segment", "filterType": "is_any_of", "filterValue": ["<segment_id>"]}
Has any CSM assigned {"fieldName": "owners_csm", "fieldType": "User", "filterType": "any_value"}

Available filter types by field type:

Field Type Filter Types
Boolean true, false
Number greater, lower, between, is_unknown, any_value
String contains, starts_with, ends_with, does_not_contain, is_unknown, any_value
Date more_than, less_than, exactly, after, before, between, on, last_week, this_week, last_month, this_month, last_quarter, this_quarter, last_year, this_year, is_unknown, any_value
Dropdown is_any_of, is_all_of, is_none_of, is_unknown, any_value
Segment is_any_of, is_all_of, is_none_of
Tag is_any_of, is_all_of, is_none_of, is_unknown, any_value
User is_in, is_not_in, is_unknown, any_value
Currency greater, lower, between, is_unknown, any_value

Contact Tools

Tool Type Description
get_contacts Read List contacts/people for an account
get_contact Read Get full contact details by ID

Health & Usage Tools

Tool Type Description
get_health_scores Read Get all health scores for an account, with score names and values
get_usage_data Read Get product usage and event data for an account
get_usage_trends Read Get health score values over time for trend analysis

Alerts & Segments

Tool Type Description
get_alerts Read Get alerts/signals for an account
get_segment_membership Read Get which segments an account belongs to

Action Tools

Tool Type Description
create_note Write Add a note to an account's timeline
create_task Write Create a task assigned to a CSM
run_playbook Write Trigger a manually-started playbook on an account
update_custom_fields Write Update custom attribute values on an account or contact

Available Resources

Resources provide read-only context that AI agents can use to understand your Custify workspace:

Resource URI Description
Segments custify://segments All segment definitions with names and IDs
Playbooks custify://playbooks All playbook definitions with names, types, and IDs
Health Score Definitions custify://health-score-definitions All health score configs with names, thresholds, and IDs

Examples

Here are real-world examples of what you can ask your AI assistant once connected:

Querying accounts

"How many churned accounts do I have?" Uses list_accounts with a churned filter to count accounts where churned = true.

"Show me all accounts with a health score below 30" Uses list_attributes to find the health score field name, then list_accounts with a Number filter.

"Find all accounts managed by jane@company.com" Uses list_accounts with a User filter on the CSM field.

"Which accounts signed up this quarter?" Uses list_accounts with a Date filter: filterType: "this_quarter" on signed_up_at.

Account deep-dives

"Give me a full summary of Acme Corp" Uses search_accounts to find Acme, then get_account, get_health_scores, get_contacts, get_usage_data, and get_segment_membership to build a comprehensive briefing.

"What segments is Acme Corp in?" Uses search_accounts to find the account ID, then get_segment_membership.

"Show me the health score trend for Acme Corp over the last month" Uses get_health_scores to find score IDs, then get_usage_trends for historical values.

Taking actions

"Create a follow-up task for Acme Corp: Review onboarding progress, due next Friday" Uses search_accounts to find Acme, then create_task with title, due date, and account ID.

"Add a note to Acme Corp: Spoke with VP of Engineering about API latency concerns" Uses search_accounts then create_note with the note body.

"Run the renewal prep playbook for Acme Corp" Uses the playbooks resource to find the playbook ID, search_accounts for the account, then run_playbook.

Discovering your data model

"What fields can I filter accounts by?" Uses list_attributes to return all available fields with their names and types.

"What segments do we have?" Reads the custify://segments resource.

"What playbooks are available?" Reads the custify://playbooks resource.


Environment Variables

Variable Required Default Description
CUSTIFY_API_KEY Yes - Your Custify API key
CUSTIFY_API_URL No https://api.custify.com Custom API base URL (for different clusters)
MCP_TRANSPORT No stdio Transport mode: stdio or streamable-http
PORT No 3000 HTTP server port (only used with streamable-http transport)

Docker

Run the server as a Docker container for HTTP-based MCP clients:

docker run -d \
  --name custify-mcp \
  -p 3000:3000 \
  -e CUSTIFY_API_KEY=your-api-key-here \
  ghcr.io/custifyofficial/custify-mcp:latest

The MCP endpoint will be available at http://localhost:3000/mcp and a health check endpoint at http://localhost:3000/health.


Security

  • Data flow: Your AI tool communicates with the Custify MCP server, which then makes authenticated API calls to the Custify REST API. No data is stored by the MCP server itself.
  • API key handling: Your CUSTIFY_API_KEY is read from environment variables and is never logged, cached, or exposed through MCP responses. When using STDIO transport, the key stays within your local process. When using HTTP transport, ensure your deployment is behind TLS.
  • Permissions: The MCP server inherits the permissions of your API key. Use a key scoped to the minimum access level your workflows require. Read-only keys will work for all read tools; write tools require a key with write permissions.
  • No telemetry: The server does not collect analytics or send data to any third party.

Troubleshooting

"Error: CUSTIFY_API_KEY environment variable is required" Make sure the CUSTIFY_API_KEY environment variable is set in your MCP client configuration. Double-check for typos and ensure there are no extra spaces.

Server not connecting in Claude Desktop

  1. Verify the config file path is correct for your OS.
  2. Ensure you have restarted Claude Desktop after editing the configuration.
  3. Check that npx is available in your system PATH.

Authentication errors (401) Your API key may be invalid or expired. Generate a new key from Custify Settings > Developer > API Access.

Permission errors (403) The endpoint may not be available for API key access. Check that your Custify account has the required permissions.

Timeout or connection errors If using HTTP transport, verify the server is running and accessible. Check that the PORT environment variable matches your deployment configuration. For STDIO transport, ensure no firewall or proxy is blocking local process communication.

Different Custify cluster? If your Custify instance is on a different cluster (e.g., EU), set CUSTIFY_API_URL to your cluster's API URL.

Docker container exits immediately Check the container logs with docker logs custify-mcp. The most common cause is a missing CUSTIFY_API_KEY environment variable.


Contributing

Contributions are welcome! To get started:

git clone https://github.com/CustifyOfficial/custify-mcp.git
cd custify-mcp-server
npm install
npm run dev
  1. Fork the repository
  2. Create a feature branch: git checkout -b feature/my-feature
  3. Make your changes and add tests
  4. Run the test suite: npm test
  5. Submit a pull request

Please open an issue first if you plan a significant change.


License

MIT - see LICENSE for details.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured