AppsFlyer MCP 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 |
|---|---|
| get_aggregate_dataC | Fetches aggregate data reports from the AppsFlyer Pull API. |
| test_appsflyer_connectionB | Test the connection to AppsFlyer API and return server status. |
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 clearly distinct purposes: one fetches aggregate data reports for analytics, while the other tests the API connection for operational status. There is no overlap or ambiguity in their functions, making it easy for an agent to select the correct tool based on the task.
Both tools follow a consistent verb_noun naming pattern (get_aggregate_data, test_appsflyer_connection), using snake_case and clear, descriptive verbs. This uniformity aids in predictability and readability across the tool set.
With only two tools, the server feels thin for an AppsFlyer analytics domain, which typically involves more operations like querying specific metrics, managing campaigns, or handling user data. The limited scope may hinder agents from performing comprehensive tasks, suggesting an underdeveloped surface.
The tool set is severely incomplete for an AppsFlyer analytics server. It lacks essential operations such as retrieving detailed reports, filtering data, managing integrations, or performing CRUD actions on resources. This gap will likely cause agent failures in handling typical analytics workflows beyond basic data fetching and connection testing.