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vom-core

storelift-mcp

get_keyword_metrics

Read-onlyIdempotent

Get demand and difficulty for one App Store search term, plus related searches, top results, rating counts, and thin or loose-match signals.

Instructions

Demand and difficulty inputs for one App Store search term in one storefront: its demand (demand) and the related searches, the top results with their rating counts and whether the term is in their title, the strongest and median rating count among them, and how deep the results go. thin marks a term with fewer than ten results; zayifEslesme marks a term the store matches loosely. Free reads the terms the account tracks in that storefront; Pro and above read any term.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ccNostorefront code, e.g. us
termYessearch term, e.g. habit tracker

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.5.0

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world), so the bar is lower; the description still adds meaningful behavior: the Free-vs-Pro gating on which terms are readable, and the semantics of the `thin` and `zayifEslesme` flags. It omits any note on cost/rate limits or freshness of the metrics.

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 tight sentences with no filler, and the core scope is front-loaded before the output enumeration. The first sentence is a long run-on list of output fields, and unexplained internal jargon (`thin`, `zayifEslesme`) slightly taxes parsing.

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?

With no output schema, the description correctly carries the return-value burden and does so thoroughly (demand, related searches, top results, rating counts, title matching, medians, result depth). What remains thin is the default value/behavior of the optional `cc` parameter and any indication of result volume or cost.

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 coverage is 100% with 2 simple params, so the schema already documents `term` and `cc` fully. The description adds only the notion that scope is a single storefront, and never clarifies what happens when the optional `cc` is omitted (default storefront, US, or account default).

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 opening clause states a specific verb-implied resource and scope: demand/difficulty inputs for one search term in one storefront, and it enumerates the returned content precisely. It does not explicitly distinguish itself from adjacent siblings like get_keyword_competition or get_search_popularity, so the agent must infer the split from the name.

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 implied by 'one search term in one storefront' and the access tier note ('Free reads the terms the account tracks...; Pro and above read any term'), which is a genuine prerequisite. However, there is no explicit when-to-use-this-vs-alternative guidance against the many keyword-related siblings.

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