Postman MCP Generator
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 |
|---|---|
| update_activity_stateC | Update the state of an activity in Adobe Target. |
| update_activity_priorityC | Update the priority of an activity in Adobe Target. |
| update_activity_scheduleC | Update the activity schedule in Adobe Target. |
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 3 tools
Each tool has a clearly distinct purpose targeting different attributes of an activity in Adobe Target: priority, schedule, and state. There is no overlap or ambiguity between them, making it easy for an agent to select the correct tool based on the specific update needed.
All tool names follow a consistent verb_noun pattern with 'update_activity_' as a prefix followed by the specific attribute (priority, schedule, state). This uniformity enhances readability and predictability across the tool set.
With only 3 tools, the set feels thin for a server named 'Postman MCP Generator', which might imply broader functionality. While the tools cover specific update operations for Adobe Target activities, the scope seems limited, lacking create, get, delete, or other essential CRUD operations for a complete activity management surface.
The tool set is severely incomplete for managing activities in Adobe Target. It only provides update operations for priority, schedule, and state, missing create, read, delete, and other lifecycle operations. This will likely cause agent failures when trying to perform full activity workflows, as there are significant gaps in coverage.