PlayFab MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| COSMOSDB_KEY | Yes | Azure Cosmos DB NoSQL account KEY from the Keys section | |
| COSMOSDB_URI | Yes | Azure Cosmos DB NoSQL account URI from the Keys section | |
| COSMOS_DATABASE_ID | Yes | Database ID from the Data Explorer section | |
| COSMOS_CONTAINER_ID | Yes | Container ID from the Data Explorer section |
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_itemsD | PlayFab search items |
| get_all_segmentsC | PlayFab get all segments |
| get_all_playersC | PlayFab get all players |
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 PlayFab resources: players, segments, and items. There is no overlap in functionality, making it easy for an agent to select the correct tool without confusion.
All tool names follow a consistent verb_noun pattern (get_all_players, get_all_segments, search_items), using snake_case throughout. This predictability enhances readability and usability.
With only 3 tools, the server feels under-scoped for a platform like PlayFab, which typically involves more operations such as creating/updating players, managing items, or handling segments. This limited set may hinder agent workflows.
The toolset is severely incomplete for PlayFab's domain, covering only retrieval operations (get_all and search) without any create, update, or delete capabilities. This leaves significant gaps that will likely cause agent failures in broader tasks.