Hosts the repository for the AppsFlyer MCP server code
Provides type-safe input validation for AppsFlyer API requests and data models
Supports testing the AppsFlyer MCP server implementation
Used as the implementation language for the AppsFlyer MCP server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@AppsFlyer MCP Server show me the daily report for the last 7 days"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server for integrating AppsFlyer analytics data with AI assistants.
Fetch aggregate data reports from AppsFlyer Pull API
Support for multiple report types: partners_report, partners_by_date_report, daily_report, geo_report, geo_by_date_report
Secure API token authentication
Type-safe input validation with Pydantic
Related MCP server: Meta Marketing API MCP Server
git clone https://github.com/ysntony/appsflyer-mcp
cd appsflyer-mcp
uv syncSet up your AppsFlyer API credentials as environment variables:
export APPSFLYER_API_BASE_URL="https://hq1.appsflyer.com"
export APPSFLYER_TOKEN="your_api_token_here"Or create a .env file:
APPSFLYER_API_BASE_URL=https://hq1.appsflyer.com
APPSFLYER_TOKEN=your_api_token_hereuv run python run_server.pyAdd to your MCP configuration file:
{
"mcpServers": {
"appsflyer": {
"command": "uv",
"args": ["run", "python", "run_server.py"],
"cwd": "/path/to/appsflyer-mcp",
"env": {
"APPSFLYER_API_BASE_URL": "https://hq1.appsflyer.com",
"APPSFLYER_TOKEN": "your_api_token_here"
}
}
}
}get_aggregate_data: Fetch aggregate data reports from AppsFlyer Pull API
test_appsflyer_connection: Test the connection to AppsFlyer API
partners_report: Partner performance data
partners_by_date_report: Daily partner performance data
daily_report: Daily aggregate data (default)
geo_report: Geographic performance data
geo_by_date_report: Daily geographic performance data
uv sync --dev
pytestMIT License
Fetches aggregate data reports from the AppsFlyer Pull API.
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'fetches aggregate data reports' which implies a read-only operation, but doesn't mention authentication requirements, rate limits, data format, pagination, or error handling. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It's appropriately sized and front-loaded with the core action, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (fetching data from an external API with multiple report types), lack of annotations, no output schema, and poor parameter documentation, the description is insufficient. It doesn't explain what 'aggregate data reports' contain, how they're structured, or provide any context about the AppsFlyer API integration.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description provides no information about parameters, while the schema has 0% description coverage (the schema's internal descriptions don't count toward this metric). With 1 required parameter (a nested object with 4 sub-parameters) and no parameter details in the description, it fails to compensate for the schema's lack of documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('fetches') and resource ('aggregate data reports from the AppsFlyer Pull API'), providing a specific purpose. However, it doesn't differentiate from the only sibling tool 'test_appsflyer_connection', which appears to be a connection test rather than a data retrieval tool, so the distinction isn't explicitly made.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives or any context for its application. It mentions the source ('AppsFlyer Pull API') but doesn't specify scenarios, prerequisites, or exclusions, leaving usage entirely implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Test the connection to AppsFlyer API and return server status.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool tests connection and returns server status, but lacks details on error handling, timeouts, authentication requirements, or what 'server status' entails (e.g., HTTP codes, latency). For a connectivity tool with zero annotation coverage, this leaves significant gaps in understanding its operational behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence: 'Test the connection to AppsFlyer API and return server status.' It is front-loaded with the core purpose, has zero redundant information, and efficiently communicates the tool's intent without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but minimal. It covers the basic purpose but lacks context on usage scenarios, error handling, or output details (e.g., format of 'server status'). For a connectivity test tool, more behavioral transparency would enhance completeness, though the absence of parameters reduces complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100% (since there are no parameters to describe). The description doesn't need to add parameter semantics, so it meets the baseline for tools with no parameters. No additional value is required beyond stating the tool's function.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Test the connection to AppsFlyer API and return server status.' It specifies the verb ('Test'), resource ('AppsFlyer API'), and outcome ('return server status'), making the function unambiguous. However, it doesn't explicitly differentiate from the sibling tool 'get_aggregate_data', which appears to be a data retrieval function rather than a connectivity test.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., authentication setup), timing (e.g., before data operations), or contrast with the sibling tool 'get_aggregate_data'. The implicit context is testing API connectivity, but explicit usage scenarios or exclusions are absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
get_aggregate_datatest_appsflyer_connectionThe 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.
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