MCP Server for Intercom
# MCP Server for Intercom
<a href="https://glama.ai/mcp/servers/@raoulbia-ai/mcp-server-for-intercom">
<img width="380" height="200" src="https://glama.ai/mcp/servers/@raoulbia-ai/mcp-server-for-intercom/badge" />
</a>
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
```bash
# 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:
```bash
# 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:**
```bash
# 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:
```bash
# 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 text
- `exclude` (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:**
```json
{
"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 date
- `endDate` (DD/MM/YYYY) – Optional end date
- `keywords` (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:**
```json
{
"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 date
- `endDate` (DD/MM/YYYY) – Optional end date
**Example:**
```json
{
"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 date
- `endDate` (DD/MM/YYYY) – Optional end date
**Example:**
```json
{
"customerIdentifier": "customer@example.com",
"startDate": "15/01/2025",
"endDate": "21/01/2025"
}
```
## Configuration with Claude Desktop
Add to your `claude_desktop_config.json`:
```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
```bash
# 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
```
## Disclaimer
This project is an independent integration and is not affiliated with, officially connected to, or endorsed by Intercom Inc. "Intercom" is a registered trademark of Intercom Inc.
## License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.TDQS
Scored across 4 tools
The tools are mostly distinct with clear purposes: list_conversations retrieves all conversations in a date range, while the other three are specific searches (by customer for conversations/tickets, by status for tickets). However, search_conversations_by_customer and search_tickets_by_customer could be slightly confused since both target customers, but their descriptions clarify the resource difference (conversations vs. tickets).
All tool names follow a consistent verb_noun pattern with snake_case: list_conversations, search_conversations_by_customer, search_tickets_by_customer, search_tickets_by_status. The naming is predictable and readable throughout the set.
With only 4 tools, the set feels thin for an Intercom server, which typically handles a broader range of operations like creating/updating conversations, managing contacts, or sending messages. While the tools cover basic retrieval and search, the count is borderline low for the domain's potential scope.
The tool surface is significantly incomplete for an Intercom integration. It only provides search and list operations, missing essential CRUD actions like create_conversation, update_ticket, or delete operations. There are also gaps in managing other Intercom resources such as contacts, companies, or messages, which will limit agent capabilities.