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AI Agents API Library

Get API details

get_api

Full entry for one service: docs, auth scheme and how the credential is sent, base URL, operations, MCP endpoint or repository (check which: a repository must be installed, a docs-only server does not call the API), free plan vs trial, rate limits, data policy, notes, last-checked dates, link check, and separate signals: uptime (our availability probes of base URL and MCP endpoint), mcp_repo (archived, last push), mcp_registry (installs) and product_reviews (rating, count, date range), each with its fetch date. Values come with the vendor page they were read from; confirm pricing and terms there before real use.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesService id from search_apis

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?

With no annotations provided, the description carries the behavioral disclosure burden. It goes beyond a field list by warning that a repository must be installed and a docs-only server does not call the API, and by noting values come from vendor pages and that pricing/terms should be confirmed there. It does not explicitly state read-only behavior, but that is reasonably implied by 'get' and the content.

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?

The description is dense and front-loaded ('Full entry for one service'), and every listed item earns its place. However, it is structured as one very long run-on sentence with nested parentheticals, which hurts skimmability enough to prevent a 5.

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?

Given there is no output schema, the description compensates well by enumerating the major output categories and important caveats such as repository installation, link checks, and fetch dates. It lacks explicit success/error behavior, but for a one-parameter lookup tool the provided context is largely sufficient.

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?

The single parameter is already fully described in the schema as 'Service id from search_apis,' and the description adds no additional parameter-level detail. With 100% schema coverage, the baseline of 3 is appropriate.

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 clearly states a specific operation: returning the full entry for one service, enumerating docs, auth, base URL, operations, rate limits, and separate signals. It implies differentiation from search/list siblings by scoping to 'one service,' but it does not explicitly name or contrast sibling tools, so it falls short of a 5.

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 context is only implied: the schema says the id comes from search_apis and the description says 'Full entry for one service,' suggesting this is the drill-down call after a search. There is no explicit guidance about when to use this tool versus search_apis, get_reviews, or list_categories, and no exclusions are stated.

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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