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Fledly

by app.fledly
OverviewDiscussionsAdmin

Suggest searches to target for an app

suggest_keywords
Read-onlyIdempotent

Returns stored suggestions for App Store searches an app could rank for: phrases from its own name, App Store autocomplete for those phrases, and phrases its closest competitors use in their titles. Apple does not publish keyword fields, so these are the public signals. An app without stored suggestions is queued for the next daily run.

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": {
      "app_id": "<app_id>"
    },
    "name": "suggest_keywords"
  }
}

Input Schema

NameRequiredDescriptionDefault
app_idYesApp Store id or apps.apple.com link.
countryNoApp Store country, two-letter code such as us, gb or fi.us
subtitleNoThe app's subtitle, if known, to widen the suggestions.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint and openWorldHint=false, so the safety profile is covered. The description adds genuinely non-obvious behavior: results are precomputed/stored rather than live, they derive from public signals because Apple publishes no keyword field, and a missing result triggers a queued daily job rather than an error. It stops short of stating freshness or how long the queue takes.

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?

Three sentences, front-loaded with what is returned, then provenance, then the empty-result edge case — no filler. The first sentence is dense with three enumerated sources in one clause, which slightly slows scanning but remains efficient.

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

Completeness4/5

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

For a simple three-parameter read tool with full schema coverage and complete annotations, the description covers output content and the queued-generation case well, which is the critical unknown. It does not describe the shape of returned suggestions (bare phrases vs. objects with metrics) despite there being no output schema to defer to.

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 app_id, country and subtitle are already documented, including that subtitle 'widens the suggestions'. The description's mention of extracting phrases 'from its own name' hints at inputs but adds no new meaning about any parameter, so the baseline of 3 applies.

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

Purpose4/5

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

The description names a specific verb and resource ('Returns stored suggestions for App Store searches an app could rank for') and enumerates the three signal sources behind those suggestions. It does not name or differentiate itself from plausible siblings like build_keyword_field or track_keywords, so an agent must infer the boundary from the word 'stored'.

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

Usage Guidelines3/5

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

Usage is only implied: an agent can infer it should call this to discover candidate search terms for an app. There is no explicit 'use this instead of X' routing, no stated prerequisite (e.g., the app must be tracked first), and no mention that build_keyword_field is the downstream consumer of these suggestions.

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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