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Get an AI tool's card

get_tool
Read-onlyIdempotent

Everything public on one EffectHub tool card: what it does, category, pricing model and entry price, Effect score, Trust score, platforms, whether it is still alive and when that was last checked, and the card URL. Takes the tool's slug (from search_tools, or the last part of https://effecthub.net/ai/{slug}).

Input Schema

NameRequiredDescriptionDefault
slugYesThe tool's slug, e.g. "cursor".

Output Schema

NameRequiredDescriptionDefault
toolYes
attributionYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=false, and destructiveHint=false, so the safety profile is covered. The description still adds useful behavioral context beyond that: it discloses liveness data ('whether it is still alive and when that was last checked') and the presence of a canonical card URL, which an agent would not infer from the annotations.

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?

Two sentences, front-loaded with the payload contents and followed by the input-sourcing note; nothing is padded. The field enumeration partially duplicates what the output schema already declares, a minor redundancy rather than real bloat.

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?

For a single-parameter, read-only lookup with an output schema, the description is complete enough: it covers the purpose, the input's provenance, and the card's key fields. Explaining return contents is not strictly required given the output schema, but it does no harm.

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% and the schema already gives a format example ('cursor'), so the baseline would be 3. The description goes further by showing how to derive the slug from an effecthub.net URL, which adds practical meaning about the value's origin and shape.

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 and resource (retrieve one EffectHub tool card) and enumerates the card's contents, so an agent knows exactly what comes back. It also implicitly separates itself from siblings by naming search_tools as the upstream feed for the slug and framing this as the single-tool lookup versus compare/list/search.

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?

It tells the agent where the required slug comes from (search_tools output, or the tail of an effecthub.net/ai/{slug} URL), which gives clear usage context. It stops short of explicit when-not guidance or a direct contrast with compare_tools and list_alternatives, so it doesn't reach the top of the scale.

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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