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

Beyond annotations (readOnlyHint, openWorldHint=false, idempotent, non-destructive), it discloses the return shape (each flag with exact passage, offsets, severity, plain structural observation), the abstention rule ('abstains (returns no flags) on any fault rather than fabricating one'), neutrality ('never adjudicates'), and that it is deterministic and local with no external lookup. This is exactly the behavioral context annotations cannot convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose and the six-flag enumeration are front-loaded and each check earns its place by telling the agent what output to expect. The trailing parenthetical about the website verifier and TrustAI engine is useful routing information but is the one section that could be trimmed; overall it is dense rather than padded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description does the necessary work: it describes the return payload (passage, offsets, severity, structural observation), the abstention behavior on ambiguous input, and determinism/no-external-lookup. For a one-parameter read-only tool whose annotations already cover safety, nothing an agent needs to invoke it correctly is missing.

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

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

There is a single parameter with 100% schema description coverage, including the 200000-char limit, so the schema already carries the semantics. The description adds only marginal detail (memo/brief/answer inputs, English and Spanish triggers), which justifies the baseline 3 rather than a higher score.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Opens with a specific verb+resource+scope: 'Independently review legal text produced by ANY AI vendor for the structural red-flags behind fabrication risk.' It then enumerates the exact six checks (orphan quotations, uncited propositions, court mismatch, impossible date, court/reporter mismatch, Avianca-listed non-existent cases), so an agent knows precisely what the tool will and won't flag. It is clearly distinct from the get_/list_ siblings on the server.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context for use (reviewing AI-generated legal text for fabrication red-flags) and explicitly states what runs elsewhere — live citation-existence checking at /verify and multi-model cross-validation in TrustAI's engine — so the agent knows when this 'load-safe tool' is and is not the right choice. It stops short of framing selection against the sibling tools, but the boundary-setting sentences are strong.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.