PBS MCP AI Enabled API 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 |
|---|---|
| pbs_apiC | Access the Australian Pharmaceutical Benefits Scheme (PBS) API to retrieve information about medicines, pricing, and availability. |
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 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear and distinct purpose, making it impossible for an agent to misselect between non-existent alternatives.
A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The tool name 'pbs_api' follows a straightforward and descriptive pattern without any conflicting conventions.
A single tool is too few for the apparent scope of accessing a comprehensive API for medicines, pricing, and availability. This minimal toolset likely forces multiple functionalities into one tool, which can be confusing and inefficient for agents trying to perform specific operations like searching, filtering, or retrieving detailed information.
The tool surface is severely incomplete for the domain of pharmaceutical benefits information. A single tool cannot adequately cover the expected operations such as searching medicines, retrieving pricing details, checking availability, or filtering by criteria, leading to significant gaps that will cause agent failures in complex tasks.