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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds critical behavioral context: the distinction between 'could_not_verify' (verification_error, not evidence) and 'unsupported' (no source found), the return format with verdicts and citations, and the automatic fallback routing. This goes well beyond the annotations and is essential for safe agent use.

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?

Although the description is longer than average, every sentence earns its place: trigger phrases, usage, routing, output semantics, and critical caller warnings. It is front-loaded with the tool's purpose and structurally organized, making it easy to parse. No filler or redundancy.

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?

Given the tool's complexity, lack of output schema, and only two parameters, the description is exceptionally complete. It explains the verdict set, the difference between failure modes, the citation mechanism, and the fallback routing. This provides everything a caller needs to understand the tool's behavior without seeing the output schema.

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?

Schema coverage is 100%, but the description enriches parameter meaning by explaining how tolerance_pct overrides the claim's implied tolerance and recommending 1-2% for hallucination detection. It also clarifies the default cap of 5%. This adds operational guidance not present in the schema alone, so a score above the baseline is warranted.

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?

The description opens with clear trigger phrases ('Is it true that…', 'fact check') and states the tool performs natural-language claim verification against authoritative sources. It explicitly distinguishes itself by describing the two routes (SEC EDGAR for company-financial claims vs. grounded pipeline for any other) and notes it replaces 4-6 sequential calls, clearly separating it from sibling tools like ask_pipeworx_grounded or deep_research.

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 an explicit usage directive: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the routing behavior and the efficiency benefit. However, it does not explicitly state when not to use the tool or name alternative tools for different scenarios, so it falls just short of a 5.

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