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AppmanAi

by com.appmanai
OverviewDiscussionsAdmin

App installs over time

app_metrics_installs_history
Read-only

An app's total installs worldwide, at most one point per day (days without data are absent), with daily — the gain per day since the previous point (null on a first point with no earlier data and on a day the total fell). For growth over the window, subtract the first total from the last. Worldwide only: there is no per-country figure. Google Play figures are Google's exact install count, and total_bucket is the range its store page shows. Apple publishes no install counts, so App Store figures are an estimate: approximate for any single app, most reliable as a trend over time or between similar apps, and to be presented to the user as an estimate. Pick the window with from / to; the last 30 days by default. Costs 10 credits.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "store": "<store>",
      "package": "<package>"
    },
    "name": "app_metrics_installs_history"
  }
}

Input Schema

NameRequiredDescriptionDefault
toNoLast day, YYYY-MM-DD, not before `from`. Today by default.
fromNoFirst day, YYYY-MM-DD. 30 days before `to` by default.
storeYesWhich app store: "android" (Google Play) or "ios" (App Store).
packageYesApp package / bundle id, e.g. "com.discord" (Android) or a numeric App Store id. If you only have the app name, call app_search first to resolve it.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the read-only annotation, the description discloses important behavior: days without data are absent, `daily` can be null on first points and falling totals, growth is computed by subtracting first `total` from last, and Apple data is an estimate with reliability caveats. It also states the credit cost and default window. These are substantive operational details not available from annotations or schema alone.

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?

The description is dense but every sentence carries operational value: what the metric is, how `daily` works, how to compute growth, geographic scope, store-specific reliability, time window, and cost. It is front-loaded with the core purpose and avoids redundant restatement of the schema.

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 the lack of an output schema, the description supplies the important semantics of the returned data, including `daily`, `total`, `total_bucket`, absent days, and Apple estimate caveats. It also covers time-window defaults and pricing. An agent has enough context to call and interpret the tool 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?

Schema coverage is 100%, so `from`, `to`, `store`, and `package` are already documented. The description adds useful meaning by explaining the default 30-day window, the absence of per-country granularity, and how `store` affects interpretation of the returned figures. It adds more than the schema but does not need to explain every parameter in depth.

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?

The description states a specific metric and resource: an app's total installs worldwide over time, with at most one point per day. It distinguishes the tool from related metrics by explaining `daily` gain versus running `total` and by noting that no per-country figure exists. An agent can identify this as the historical installs tool without opening the schema.

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?

It gives clear usage context: worldwide only, default last 30 days, window selected with `from`/`to`, and a cost of 10 credits. It also explains that Apple figures are estimates to be presented as such and that Google Play figures are exact. It does not explicitly route the agent to sibling alternatives such as revenue or ratings history, which keeps it from a 5.

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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