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storelift-mcp

storelift-mcp

by vom-core
OverviewSchemaSourceRelated ServersDiscussions(1)Admin
JavaScript
Remote

get_keyword_competition

Read-onlyIdempotent

Count how many apps compete for each keyword in one storefront, using tracked terms or comma-separated candidates; returns counts and corpus size.

Instructions

How many apps compete on each keyword in one storefront, returned for terms seen on two or more apps. Defaults to the app's tracked keywords; pass terms to count candidates before you track them. corpus is the size of the set behind the count.

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": {
      "appId": "<appId>"
    },
    "name": "get_keyword_competition"
  }
}

Input Schema

NameRequiredDescriptionDefault
appIdYesapp id from list_apps
termsNoup to 100 terms, comma separated (defaults to the tracked keywords)
countryNocountry code, e.g. tr, us (defaults to the app's first country)
platformNodefaults to ios

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.5.0

TDQS

A4/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, closed-world), so the bar is lower, and the description adds genuine semantics: the result is scoped to one storefront, restricted to terms seen on two or more apps, and includes a `corpus` figure as the size of the underlying set. These are non-obvious behavioral details an agent couldn't get from the schema.

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 that front-load the core definition before touching defaults and the corpus field. No padding or repetition, though the back-reference to `corpus` in the last sentence is slightly disjointed from the parameter list it never appears in.

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 carries the return-value burden and does explain what is counted and what `corpus` means. Defaults for country, platform and terms are all covered. A quick note on result ordering or the practical size limit of the response would fully close it out.

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 baseline is 3, but the description adds meaning beyond the schema: it frames `terms` as the way to evaluate candidates before tracking, whereas the schema only states the plain default. It also clarifies that the term set and the count are storefront-bound, linking `country` scope to the output.

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?

States a specific verb+resource: counting how many apps compete per keyword within a single storefront. The 'two or more apps' scoping rule sharpens what the count means. It does not name or differentiate itself from metric siblings like get_keyword_metrics, get_keyword_gap, or get_search_popularity, so it stops short of 5.

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?

Gives clear mode-selection guidance: default is the app's tracked keywords, and `terms` is the route for counting candidates before tracking them. That is real context for choosing an input path. It still offers no when-not or comparison to the competing keyword-metric siblings.

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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