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Fledly

by app.fledly
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Find young apps that already earn

find_young_earners
Read-onlyIdempotent

Lists iOS apps released in the last months that already rank on the App Store Top Grossing charts, with estimated monthly revenue, age, rating growth, chart rank, prices and top review complaints. Big publishers are excluded. total_matches and by_genre summarize every match, and offset and next_offset page through the list. estimate_confidence "low" marks apps with under 50 ratings, suspected paid installs or no overall-chart anchor, whose revenue estimate is unreliable. lookalike_name and reissued_listing flag imitation names and old listings re-released as new.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "find_young_earners"
  }
}

Input Schema

NameRequiredDescriptionDefault
sortNorevenue = estimated revenue, growth = new ratings per month, newest = release date.revenue
genreNoApp Store category, for example Health & Fitness or Photo & Video.
limitNoHow many apps to return per page.
queryNoWords to match in the app name, subtitle or category.
offsetNoNumber of matches to skip; results give next_offset for the next page.
countryNoApp Store country (two-letter code).us
max_age_monthsNoOnly apps released at most this many months ago.
released_afterNoOnly apps released on or after this date (YYYY-MM-DD), e.g. 2026-01-01 for "released this year".
min_monthly_revenue_usdNoMinimum estimated monthly revenue in USD.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the readOnly/idempotent/non-destructive annotations, the description discloses concrete behavior: total_matches and by_genre summarize all matches, offset and next_offset drive paging, estimate_confidence "low" marks unreliable revenue estimates under specific conditions (under 50 ratings, paid installs, no chart anchor), and lookalike_name/reissued_listing flag imitations and re-releases. This is substantial context an agent could not infer from structured fields.

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

Conciseness4/5

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

Front-loaded with purpose and scope, then the return fields, then pagination and quality flags. Dense but every sentence carries information; a few sentences (estimate_confidence, lookalike_name) are long but justified by their specificity.

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?

With no output schema, the description takes on the burden of explaining return values and does so thoroughly (revenue, age, rating growth, chart rank, prices, review complaints, total_matches, by_genre, next_offset). Combined with full schema coverage for all 9 parameters, an agent has enough to invoke and interpret the tool correctly.

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

Parameters3/5

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

Schema description coverage is 100%, so every parameter including sort, genre, offset, and max_age_months is already documented. The description adds context around how offset/next_offset paginate and how estimate_confidence relates to data quality, but does not add new meaning to most individual parameters, so the baseline 3 applies.

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?

States a specific verb and resource: lists iOS apps released in recent months that already rank on Top Grossing charts. It names the qualifying filters (excludes big publishers) and the returned metrics, so an agent can distinguish it from siblings like get_top_charts or search_app_store.

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?

The description makes clear the usage context (finding young apps with existing revenue) and notes that big publishers are excluded, which sets expectations. It does not explicitly name sibling alternatives or state when NOT to use this tool, so it falls short of 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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