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Glama
raoulbia-ai

MCP Server for Intercom

by raoulbia-ai

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
INTERCOM_ACCESS_TOKENYesYour Intercom API token (available in your Intercom account settings)

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Server capabilities have not been inspected yet.

Tools

Functions exposed to the LLM to take actions

NameDescription
search_conversations_by_customerA

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.

search_tickets_by_statusA

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.

list_conversationsA

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.

search_tickets_by_customerB

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.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

A3.5/5.0

Scored across 4 tools

Disambiguation4/5

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).

Naming Consistency5/5

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.

Tool Count3/5

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.

Completeness2/5

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.

Maintenance

ActivityInactive
ResponsivenessUnresponsive