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AppTail – ASO & Keyword Research

Remove Market

remove_market
Destructive

Delete a saved market, or just one of its storefronts. Pass store to drop a single storefront and leave the rest of the market with its corpora and its build clocks untouched; omit it to delete the whole market. Deleting frees a slot on the account's plan. The terms themselves are not deleted — they are shared with any other market or tracked app using them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
storeNoDrop only this storefront, ISO 3166-1 alpha-2. Omit to delete the whole market. Dropping the last storefront deletes the market too, because a market asked nowhere is not a market.
market_idYesApptail market id — from list_markets.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
deletedYesTrue when the whole market is gone. False when only a storefront was dropped and the market remains.
market_idYes
markets_remainingYesMarkets left on the account after this call.
removed_storefrontsYesWhat this call removed.
remaining_storefrontsYesWhat the market is still asked in.

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?

Goes well beyond the destructiveHint=true annotation by disclosing that deletion frees a plan slot, that shared terms survive deletion, and that dropping a single storefront preserves the rest of the market's corpora and build clocks. This is exactly the kind of side-effect and shared-resource context an agent needs before a destructive call.

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?

Three tight sentences, front-loaded with the dual behavior, then consequences. Every sentence carries distinct information (mode selection, plan-slot effect, term sharing).

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?

An output schema exists, so return values need not be described. Combined with destructiveHint annotations, the description covers mode selection, side effects, shared-resource caveats, and plan implications – everything needed to invoke safely.

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 the schema already documents both params (baseline 3). The description adds meaning beyond the schema by explaining the storefront-level outcome of `store` (rest of market and build clocks untouched), which the schema does not fully spell out.

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+resource (delete a saved market or one storefront) and clearly distinguishes the two modes of operation. An agent can tell it apart from sibling tools like save_market, get_market, and list_markets without opening a 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?

Explicitly states when to pass `store` versus omitting it, and what each choice leaves untouched. It doesn't name sibling alternatives (e.g., save_market or remove_keywords) as routing options, so it stops short of full when/when-not guidance, but the operational context is clear.

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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