The MCP Server for Intercom enables AI assistants to access and analyze customer support data from Intercom with advanced search and filtering capabilities:
Search Intercom conversations by specific customers, keywords, date ranges, or email content (even when no contact exists)
Filter Intercom tickets by status (open, pending, resolved) or associated customer
Retrieve conversation history within specific date ranges (max 7 days)
Utilize efficient server-side filtering via Intercom's search API
Integrate seamlessly with MCP-compliant AI assistants for efficient customer support data analysis
Enables access and analysis of customer support data from Intercom, with capabilities for searching conversations and tickets using advanced filters, filtering by customer, status, date range, and keywords, and searching email content even without existing contacts.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Server for Intercom show me open tickets from the last 3 days"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP-compliant server that enables AI assistants to access and analyze customer support data from Intercom.
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
Related MCP server: jitbit-helpdesk-mcp
Node.js 18.0.0 or higher
An Intercom account with API access
Your Intercom API token (available in your Intercom account settings)
# 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-mcpThe 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:latestValidation 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:3000If 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:latestThe default version includes specific dependencies and configurations required for integration with the Glama platform, while the standard version is more lightweight.
list_conversationsRetrieves 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:
{
"startDate": "15/01/2025",
"endDate": "21/01/2025",
"keyword": "billing"
}search_conversations_by_customerFinds 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:
{
"customerIdentifier": "customer@example.com",
"startDate": "15/01/2025",
"endDate": "21/01/2025",
"keywords": ["billing", "refund"]
}search_tickets_by_statusRetrieves 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:
{
"status": "open",
"startDate": "15/01/2025",
"endDate": "21/01/2025"
}search_tickets_by_customerFinds 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:
{
"customerIdentifier": "customer@example.com",
"startDate": "15/01/2025",
"endDate": "21/01/2025"
}Add to your claude_desktop_config.json:
{
"mcpServers": {
"intercom-mcp": {
"command": "intercom-mcp",
"args": [],
"env": {
"INTERCOM_ACCESS_TOKEN": "your_intercom_api_token"
}
}
}
}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.
# 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 testThis 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.
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
Retrieves Intercom conversations within a specific date range.
Required: startDate, endDate (DD/MM/YYYY format, max 7-day range) Optional: keyword, exclude (for content filtering)
Always ask for specific dates when user makes vague time references.
| Name | Required | Description | Default |
|---|---|---|---|
| endDate | Yes | End date in DD/MM/YYYY format (e.g., '21/01/2025'). Required. | |
| exclude | No | Optional exclusion filter for conversation content. | |
| keyword | No | Optional keyword to filter conversations by content. | |
| startDate | Yes | Start date in DD/MM/YYYY format (e.g., '15/01/2025'). Required. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It adds some context beyond basic functionality: it specifies the date format (DD/MM/YYYY), max range (7 days), and that parameters are required or optional. However, it doesn't cover important behavioral aspects like rate limits, authentication needs, pagination, or what the return format looks like (especially since there's no output schema). For a tool with no annotations, this leaves gaps in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by details on parameters and a usage directive. Each sentence adds value, with no wasted words. It could be slightly more structured (e.g., bullet points for parameters), but it's efficient and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a retrieval tool with 4 parameters), no annotations, and no output schema, the description is somewhat complete but has gaps. It covers the basic purpose, parameter requirements, and a usage tip, but lacks details on behavioral traits (e.g., rate limits), output format, and how it differs from siblings. For a tool without structured support, it should do more to compensate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, meaning the input schema already documents all parameters thoroughly. The description adds minimal value beyond the schema: it reiterates that startDate and endDate are required and in DD/MM/YYYY format, and mentions the max 7-day range (which isn't in the schema). However, it doesn't provide additional semantic context for the optional parameters (keyword, exclude) or explain their interactions. Baseline 3 is appropriate when the schema does most of the work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Retrieves Intercom conversations within a specific date range.' This specifies the verb ('retrieves'), resource ('Intercom conversations'), and scope ('within a specific date range'). However, it doesn't explicitly differentiate from sibling tools like 'search_conversations_by_customer,' which appears to be a more targeted search tool, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: it specifies that the tool is for retrieving conversations within a date range and includes a directive to 'Always ask for specific dates when user makes vague time references.' This offers practical guidance on when to use it (for date-based retrieval) and how to handle ambiguous inputs. However, it doesn't explicitly state when not to use it or mention alternatives like the sibling tools, so it's not a perfect 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Searches for conversations by customer email or ID with optional date filtering.
Required: customerIdentifier (email/ID) Optional: startDate, endDate (DD/MM/YYYY format) Optional: keywords (array of terms to filter by)
Use when looking for conversation history with a specific customer.
| Name | Required | Description | Default |
|---|---|---|---|
| customerIdentifier | Yes | Customer email or ID to search for | |
| endDate | No | Optional end date in DD/MM/YYYY format (e.g., '21/01/2025') | |
| keywords | No | Optional keywords to filter conversations by content | |
| startDate | No | Optional start date in DD/MM/YYYY format (e.g., '15/01/2025') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions optional date filtering and keywords, but lacks critical behavioral details like whether this is a read-only operation, what permissions are needed, how results are returned (e.g., pagination), or error handling. For a search tool with no annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Perfectly concise and well-structured: first sentence states purpose, bullet points clarify parameters, and final sentence provides usage guidance. Every sentence earns its place with zero waste, and information is front-loaded appropriately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a search tool. It covers purpose and parameters well, but misses behavioral aspects like result format, pagination, or error cases. However, it's adequate for basic usage, so it meets the minimum viable threshold.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value by restating that customerIdentifier is required and dates/keywords are optional, but doesn't provide additional context beyond what's in the schema. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('searches') and resource ('conversations'), and distinguishes it from siblings by specifying it searches by customer email/ID rather than listing all conversations or searching tickets. The title being null doesn't affect this clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use this tool ('Use when looking for conversation history with a specific customer'), which differentiates it from sibling tools like list_conversations (general listing) and search_tickets_by_customer (ticket-focused). No when-not guidance, but the context is clear enough for full credit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Searches for tickets by customer email or ID with optional date filtering.
Required: customerIdentifier (email/ID) Optional: startDate, endDate (DD/MM/YYYY format)
Use when analyzing a customer's support history.
| Name | Required | Description | Default |
|---|---|---|---|
| customerIdentifier | Yes | Customer email or ID to search for | |
| endDate | No | Optional end date in DD/MM/YYYY format (e.g., '21/01/2025') | |
| startDate | No | Optional start date in DD/MM/YYYY format (e.g., '15/01/2025') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions the tool is for searching (implying read-only), it doesn't disclose important behavioral traits like whether this is a safe read operation, if it requires specific permissions, rate limits, pagination behavior, or what the response format looks like. The description adds minimal behavioral context beyond the basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with three sentences that are front-loaded with the core purpose. Each sentence earns its place by providing purpose, parameter guidance, and usage context. Minor improvements could include combining the parameter details into a single sentence for better flow.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is somewhat complete but has gaps. It covers the purpose and basic usage but lacks details on behavioral traits, response format, and explicit differentiation from sibling tools. The absence of annotations and output schema increases the need for more comprehensive description, which isn't fully met.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all parameters. The description adds some value by explicitly labeling parameters as 'Required' and 'Optional' and specifying the date format, but this information is largely redundant with the schema. The baseline score of 3 reflects adequate but not exceptional added semantic value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches for tickets by customer email or ID with optional date filtering, providing a specific verb ('searches') and resource ('tickets'). It distinguishes from sibling tools like 'search_tickets_by_status' by specifying customer-based search, though it doesn't explicitly mention how it differs from 'search_conversations_by_customer' beyond the resource type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool ('when analyzing a customer's support history'), which helps guide usage. However, it doesn't explicitly state when NOT to use it or mention specific alternatives among the sibling tools, such as when to choose 'search_conversations_by_customer' instead.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Searches for tickets by status with optional date filtering.
Required: status (one of: open, pending, resolved) Optional: startDate, endDate (DD/MM/YYYY format)
Use when analyzing support workload or tracking issue resolution.
| Name | Required | Description | Default |
|---|---|---|---|
| endDate | No | Optional end date in DD/MM/YYYY format (e.g., '21/01/2025') | |
| startDate | No | Optional start date in DD/MM/YYYY format (e.g., '15/01/2025') | |
| status | Yes | Ticket status to search for (open, pending, or resolved) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions the search functionality and date format, it lacks critical behavioral details: it doesn't specify whether this is a read-only operation, what permissions might be required, how results are returned (e.g., pagination, format), or any rate limits. For a search tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with three sentences: purpose statement, parameter requirements, and usage guidelines. Each sentence adds distinct value without redundancy. It's appropriately sized and front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is moderately complete for a search tool. It covers the basic purpose, parameters, and usage context, but lacks details about behavioral aspects (e.g., read-only nature, result format, error handling) and doesn't fully address sibling tool differentiation. For a tool with 3 parameters and no structured safety hints, more behavioral context would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with all parameters well-documented in the schema (status with enum values, startDate/endDate with format). The description adds minimal value beyond the schema: it repeats the status options and date format, but doesn't provide additional context like how date filtering interacts with status or example use cases for the parameters. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Searches for tickets by status with optional date filtering.' This specifies the verb ('searches'), resource ('tickets'), and scope ('by status with optional date filtering'). However, it doesn't explicitly differentiate from sibling tools like 'search_tickets_by_customer' or 'search_conversations_by_customer', which would require mentioning customer vs. status filtering.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context: 'Use when analyzing support workload or tracking issue resolution.' This gives practical scenarios for when to use the tool. However, it doesn't explicitly state when NOT to use it or mention alternatives like the sibling tools, which would be needed for a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0list_conversationssearch_conversations_by_customersearch_tickets_by_customersearch_tickets_by_statusScored 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.
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