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

Annotations already declare the safety profile (readOnlyHint=true, openWorldHint=true, destructiveHint=false), and the description adds meaningful context on top: the exact feed sources ingested, the LLM-classification behavior, the ranking logic ('ranked by relevance then recency'), and the important caveat that this is a 'triage signal, not advice.' It also discloses bounded limits (e.g., 720h cap) without contradicting annotations.

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 description is long but every sentence serves a purpose: sources, output shape, ranking, parameters, and workflow guidance. The key summary is front-loaded for rapid triage. The 'Not financial advice' repetition is slightly redundant, but with the tool's complexity (2 companies, 6 categories, 4 parameters), the density is mostly justified.

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?

For a tool with an output schema and rich annotations, the description covers all critical ground: what it returns, how it ranks, parameters, and workflow context. An agent can confidently decide when to call it and what to expect. Minor gaps include lack of explicit prerequisites or rate-limit information, but nothing essential is missing for successful invocation.

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?

With 0% schema description coverage, the description correctly carries the full burden of parameter documentation and does so thoroughly. It explains window_hours as a bound on lookback with defaults and caps, min_relevance as a filtering floor, and category as an optional narrowing. Since these constitute the entire parameter story, this is exactly what compensation for low coverage looks like.

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 a specific, non-tautological statement: 'Recent technology / tooling developments relevant to Crank + Engaij (free read).' It details the exact sources scanned, the classification schema, and the output fields, making it unambiguous what this tool does and distinguishes it from every sibling tool without naming one. An agent would know immediately when this is the right tool.

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 states the workflow placement: 'step -- scan for SDK / API releases, competitor moves, or tooling worth adopting before planning integration or strategy work.' It gives strong contextual guidance on when to use it, though it doesn't explicitly name alternative tools to disambiguate against (e.g., get_regulatory_updates). Clear context without explicit exclusions.

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