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asif786ka
by asif786ka

verify_api_exists

Confirm whether an API is real and usable on a specific version to prevent hallucinated code. Returns availability verdict with migration target if deprecated.

Instructions

Anti-hallucination check. Confirms whether an API is real AND usable on a specific version before an agent writes code against it.

Args: name: symbol name, e.g. "scrollTargetBehavior" or "animateItem". target_version: the version to check against ("17.0", "1.6.0", "34"). platform: "android" | "apple" | "" to search both.

Returns a verdict: available / deprecated / not-yet-available / unknown, with the migration target when relevant.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
platformNo
target_versionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description fully discloses behavior: it returns a verdict (available, deprecated, etc.) and migration target. It also labels itself as an 'anti-hallucination check', which is a behavioral trait, but lacks details on potential side effects (though none expected).

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?

Description is concise: a one-line summary, followed by a clear list of arguments and return value. Every sentence adds value, and the structure is front-loaded with the purpose.

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?

Given the existence of an output schema (not shown but known), the description is complete: it covers purpose, usage context, parameter semantics, and return value. No gaps remain for an agent to correctly select and invoke this tool.

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

Parameters5/5

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

Input schema has 0% description coverage, but the description fully explains each parameter: 'name' with examples like 'scrollTargetBehavior', 'target_version' with version format examples, and 'platform' with allowed values. This adds significant meaning beyond the schema.

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?

Description clearly states the tool's purpose as an 'Anti-hallucination check' that confirms API existence and usability on a specific version, distinguishing it from siblings like get_api_reference which likely provides full reference details.

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?

Description explicitly says to use 'before an agent writes code against it', providing clear context. It does not explicitly state when not to use or list alternatives, but the purpose is well-defined and the handling of 'unknown' verdict implies caution.

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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