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Get one Trustcase use case

get_use_case
Read-only

Retrieve the full Trustcase entry for an AI agent use case by slug to check its trust score, risks, human approval steps, and setup details.

Instructions

Get the full Trustcase entry for one AI agent use case by slug (from search_use_cases). Use it to answer whether a task is safe to hand to an agent, what could go wrong, what needs human approval, and how to set it up. Returns: the 0 to 100 trust score with its breakdown (access, human approval, undo and logs, scope, each with a reason), what the agent does on its own, what waits for a human yes, suggested tools, models and apps it works in, a copyable setup prompt, and the trustcase.dev link. Read-only.

Input Schema

NameRequiredDescriptionDefault
slugYesUse case slug from search_use_cases, for example 'lead-research-brief'

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already cover readOnlyHint and openWorldHint, so the trailing 'Read-only' adds little. The real added value is the detailed disclosure of what the payload contains — trust score with per-dimension reasoning, autonomy vs. human-approval split, suggested tools, setup prompt, and external link.

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?

Front-loaded with purpose, then usage, then a structured 'Returns:' enumeration. The returns list is long but each item is a distinct content category an agent would otherwise have to discover by calling; only the redundant 'Read-only' clause is wasted.

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?

There is no output schema, so the description must describe the response — and it does so thoroughly, covering score breakdown, autonomy/approval split, recommendations, and prompt. Nothing material is missing for a single-parameter read tool.

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?

Schema description coverage is 100% and already documents the slug with an example, so the schema carries the load. The description only adds the provenance hint that slugs originate from search_use_cases, which is marginal 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?

Specific verb and resource: 'Get the full Trustcase entry for one AI agent use case by slug'. It also distinguishes itself from the only sibling by noting the slug comes from search_use_cases, so an agent can route between retrieval and search without inspecting schemas.

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 concrete framing for when to reach for it: answering whether a task is safe to delegate, what could go wrong, what needs human approval, and setup. The alternative (search_use_cases) is referenced as the slug source but no explicit 'use X instead when...' exclusion is 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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