MCP Intercom Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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
| Name | Description |
|---|---|
| search-conversationsB | Search Intercom conversations with filters for created_at, updated_at, source type, state, open, and read status |
| list-conversations-from-last-weekB | Fetch all conversations from the last week (last 7 days) |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
The two tools have distinct primary purposes: one fetches recent conversations by time window, while the other searches with multiple filters. However, there is some overlap since 'list-conversations-from-last-week' could be seen as a subset of what 'search-conversations' can do with a created_at filter, which might cause minor confusion.
Both tools use kebab-case and follow a verb-noun pattern (list-conversations, search-conversations), which is consistent. The addition of 'from-last-week' in the first tool name is descriptive but breaks the pure verb-noun convention slightly, though it remains readable.
With only 2 tools, the server feels under-scoped for an Intercom integration. It lacks essential operations like creating, updating, or replying to conversations, which are core to customer support workflows, making the toolset too thin for the apparent domain.
The server only provides read-only access to conversations, missing critical CRUD operations such as creating conversations, sending messages, updating conversation states, or managing users. This leaves significant gaps that will hinder agents from performing common Intercom tasks.