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pavellunev99

appmetrica-mcp

by pavellunev99

export_events

Export raw event logs from AppMetrica. Filter by date range and event name to analyze in-app purchases and subscriptions.

Instructions

Export raw event logs from AppMetrica Logs API. Revenue and in-app purchase data is stored in the event_json field as structured JSON. Default fields: event_name, event_datetime, event_json, appmetrica_device_id, app_version_name, os_version, device_model, country_iso_code, city. To get subscription/purchase data: filter by event_name (e.g. "subscription_purchase") and parse event_json field.

Logs API is asynchronous — the first request usually returns "data not ready" and the result must be re-polled (this wrapper waits up to 60s, so a busy or wide query may need a manual retry after a few minutes). Start with 1-day windows; multi-day exports take noticeably longer to materialise. Run export_events sequentially — 4+ parallel calls hit HTTP 429.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
app_idYesAppMetrica application ID
date_fromYesStart date YYYY-MM-DD
date_toYesEnd date YYYY-MM-DD
event_nameNoFilter by specific event name (e.g. subscription_purchase)
fieldsNoComma-separated fields to return. Defaults: event_name,event_datetime,event_json,appmetrica_device_id,app_version_name,os_version,device_model,country_iso_code,city
limitNoMaximum number of events to return (default 1000)
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description fully covers behavior: async with re-polling (60s wait), rate limiting (429), default fields, revenue data structure in event_json, and performance characteristics. Comprehensive disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise, well-structured paragraphs. Front-loaded with main purpose, then details. Every sentence adds value with zero redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and 6 parameters, description covers async behavior, rate limits, defaults, filtering, and performance advice. Complete enough for an agent to use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema coverage is 100% so baseline 3. Description adds value: explains that event_name filters (e.g., 'subscription_purchase'), event_json contains structured JSON, and lists default fields. Adds meaningful context beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Starts with 'Export raw event logs from AppMetrica Logs API', clearly stating verb (export) and resource (raw event logs). Distinguishes from sibling export tools like export_crashes and export_installations by specifying events.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit usage tips: start with 1-day windows, run sequentially, avoid 4+ parallel calls due to HTTP 429. Also explains async nature and re-polling. Does not explicitly compare to alternatives, but context implies this is for events.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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