Access Intercom Customer Data for AI
An MCP server that gives AI assistants access to Intercom customer support data: search conversations and tickets by customer, status, date, or keyword.
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Why it matters
Enable AI assistants to access and analyze customer support data from Intercom, including conversations and tickets. Provides advanced filtering capabilities for efficient data retrieval.
Outcomes
What it gets done
Retrieve conversations with keyword and content filtering.
Search conversations and tickets by customer email or ID.
Filter tickets by status and date range.
Integrate with MCP-compatible AI assistants.
Source
Get it from source
Spark does not host a copy of it.
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Capabilities
Tools your agent gets
Retrieves all conversations within a specified date range with content filtering by keywords and exclusions.
Finds conversations for a specific customer by email or Intercom ID with optional date range filtering.
Retrieves tickets by their status (open, pending, or resolved) with optional date range filtering.
Finds tickets associated with a specific customer by email or Intercom ID with optional date range filtering.
Overview
Intercom MCP Server
This MCP-compliant server allows AI assistants to access and analyze customer support data from Intercom. It provides tools to search conversations and tickets with advanced filtering options, including by customer, status, date range, and keywords. It can even search by email content when no contact exists. Use this server when you need to integrate AI assistants with Intercom customer support data. It's ideal for scenarios requiring programmatic access to conversations and tickets for analysis, reporting, or automated response generation.
What it does
An MCP-compliant server that lets AI assistants access and analyze customer support data from Intercom. Four tools: list_conversations retrieves conversations within a date range (max 7 days) with keyword include/exclude filtering; search_conversations_by_customer finds conversations for a specific customer by email or Intercom ID, even resolving conversations found by email content when no contact record exists; search_tickets_by_status retrieves tickets by status (open, pending, or resolved), optionally within a date range; search_tickets_by_customer finds tickets tied to a specific customer.
Inputs and outputs
Requires Node.js 18+ and an Intercom API token. Install and run via npm:
npm install -g mcp-server-for-intercom
export INTERCOM_ACCESS_TOKEN="your_token_here"
intercom-mcp
Or via Docker (a Glama-optimized default image and a lighter Dockerfile.standard are both provided). Configure in claude_desktop_config.json:
{ "mcpServers": { "intercom-mcp": { "command": "intercom-mcp", "args": [], "env": { "INTERCOM_ACCESS_TOKEN": "your_intercom_api_token" } } } }
All search tools use Intercom's server-side search API for efficient filtering rather than pulling full result sets client-side.
Who it's for
Support and customer-success teams who want an AI assistant to pull and analyze Intercom conversation/ticket data directly, without building a custom Intercom integration. Apache 2.0 licensed; an independent integration, not affiliated with or endorsed by Intercom Inc.
Docker deployment
Two Docker images are provided: a default one optimized for Glama platform compatibility, and a lighter Dockerfile.standard without Glama-specific dependencies.
docker build -t mcp-intercom .
docker run --rm -it -p 3000:3000 -p 8080:8080 -e INTERCOM_ACCESS_TOKEN="your_token_here" mcp-intercom:latest
After starting, validate the deployment:
curl -v http://localhost:8080/.well-known/glama.json
curl -X POST -H "Content-Type: application/json" -d '{"jsonrpc":"2.0","id":1,"method":"mcp.capabilities"}' http://localhost:3000
For detailed technical information about how the server integrates with Intercom's API - parameter mapping, endpoint usage, and implementation notes - see src/services/INTERCOM_API_NOTES.md in the repository.
Example calls
{ "startDate": "15/01/2025", "endDate": "21/01/2025", "keyword": "billing" }
for list_conversations, filtering to conversations mentioning "billing" in a one-week window;
{ "customerIdentifier": "customer@example.com", "startDate": "15/01/2025", "endDate": "21/01/2025", "keywords": ["billing", "refund"] }
for search_conversations_by_customer, which resolves an email to a contact ID for efficient searching even when the keyword match came from message content rather than a stored contact field; and
{ "status": "open", "startDate": "15/01/2025", "endDate": "21/01/2025" }
for search_tickets_by_status, one of "open", "pending", or "resolved".
When to use - and when NOT to
Use it when an AI assistant needs real Intercom data - recent conversations, open tickets, a specific customer's history - to answer a support or account question. Date ranges are capped at 7 days for conversation search, so a broader historical analysis needs multiple calls stitched together rather than one wide query.
Source README
MCP Server for Intercom
An MCP-compliant server that enables AI assistants to access and analyze customer support data from Intercom.
Features
- Search conversations and tickets with advanced filtering
- Filter by customer, status, date range, and keywords
- Search by email content even when no contact exists
- Efficient server-side filtering via Intercom's search API
- Seamless integration with MCP-compliant AI assistants
Installation
Prerequisites
- Node.js 18.0.0 or higher
- An Intercom account with API access
- Your Intercom API token (available in your Intercom account settings)
Quick Setup
Using NPM
# Install the package globally
npm install -g mcp-server-for-intercom
# Set your Intercom API token
export INTERCOM_ACCESS_TOKEN="your_token_here"
# Run the server
intercom-mcp
Using Docker
The default Docker configuration is optimized for Glama compatibility:
# Start Docker (if not already running)
# On Windows: Start Docker Desktop application
# On Linux: sudo systemctl start docker
# Build the image
docker build -t mcp-intercom .
# Run the container with your API token and port mappings
docker run --rm -it -p 3000:3000 -p 8080:8080 -e INTERCOM_ACCESS_TOKEN="your_token_here" mcp-intercom:latest
Validation Steps:
# Test the server status
curl -v http://localhost:8080/.well-known/glama.json
# Test the MCP endpoint
curl -X POST -H "Content-Type: application/json" -d '{"jsonrpc":"2.0","id":1,"method":"mcp.capabilities"}' http://localhost:3000
Alternative Standard Version
If you prefer a lighter version without Glama-specific dependencies:
# Build the standard image
docker build -t mcp-intercom-standard -f Dockerfile.standard .
# Run the standard container
docker run --rm -it -p 3000:3000 -p 8080:8080 -e INTERCOM_ACCESS_TOKEN="your_token_here" mcp-intercom-standard:latest
The default version includes specific dependencies and configurations required for integration with the Glama platform, while the standard version is more lightweight.
Available MCP Tools
1. list_conversations
Retrieves all conversations within a date range with content filtering.
Parameters:
startDate(DD/MM/YYYY) - Start date (required)endDate(DD/MM/YYYY) - End date (required)keyword(string) - Filter to include conversations with this textexclude(string) - Filter to exclude conversations with this text
Notes:
- Date range must not exceed 7 days
- Uses efficient server-side filtering via Intercom's search API
Example:
{
"startDate": "15/01/2025",
"endDate": "21/01/2025",
"keyword": "billing"
}
2. search_conversations_by_customer
Finds conversations for a specific customer.
Parameters:
customerIdentifier(string) - Customer email or Intercom ID (required)startDate(DD/MM/YYYY) - Optional start dateendDate(DD/MM/YYYY) - Optional end datekeywords(array) - Optional keywords to filter by content
Notes:
- Can find conversations by email content even if no contact exists
- Resolves emails to contact IDs for efficient searching
Example:
{
"customerIdentifier": "customer@example.com",
"startDate": "15/01/2025",
"endDate": "21/01/2025",
"keywords": ["billing", "refund"]
}
3. search_tickets_by_status
Retrieves tickets by their status.
Parameters:
status(string) - "open", "pending", or "resolved" (required)startDate(DD/MM/YYYY) - Optional start dateendDate(DD/MM/YYYY) - Optional end date
Example:
{
"status": "open",
"startDate": "15/01/2025",
"endDate": "21/01/2025"
}
4. search_tickets_by_customer
Finds tickets associated with a specific customer.
Parameters:
customerIdentifier(string) - Customer email or Intercom ID (required)startDate(DD/MM/YYYY) - Optional start dateendDate(DD/MM/YYYY) - Optional end date
Example:
{
"customerIdentifier": "customer@example.com",
"startDate": "15/01/2025",
"endDate": "21/01/2025"
}
Configuration with Claude Desktop
Add to your claude_desktop_config.json:
{
"mcpServers": {
"intercom-mcp": {
"command": "intercom-mcp",
"args": [],
"env": {
"INTERCOM_ACCESS_TOKEN": "your_intercom_api_token"
}
}
}
}
Implementation Notes
For detailed technical information about how this server integrates with Intercom's API, see src/services/INTERCOM_API_NOTES.md. This document explains our parameter mapping, Intercom endpoint usage, and implementation details for developers.
Development
# Clone and install dependencies
git clone https://github.com/raoulbia-ai/mcp-server-for-intercom.git
cd mcp-server-for-intercom
npm install
# Build and run for development
npm run build
npm run dev
# Run tests
npm test
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