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

The annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description goes beyond that by disclosing how results are presented: 'separately ranked groups with source dates and canonical links.' It also states an important behavioral nuance: 'without pretending their commands or authority models are identical,' which is valuable context for users expecting a unified answer. No contradictions.

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?

Two sentences, front-loaded with the core purpose, then usage examples, then output details. Every clause adds value without padding. The structure is ideal for quick scanning in a tool list.

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 there is an output schema (context signal) and detailed annotations, the description doesn't need to explain return formats or safety. It covers purpose, typical usage, and output grouping. The main gap is the lack of parameter-level detail (see parameter_semantics), but overall the tool is well-understood from the description alone. A complete description would at least hint at the 'products' filter.

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 description coverage is 0%, so the description must compensate for undocumented parameters. It explains the general purpose ('task' implies the query) but never mentions the 'products' array (allowing a subset of Claude Code, Codex, Grok Build), the 'limit_per_product' parameter, or the 'language' parameter. Users cannot infer these options purely from the description; they'd need to inspect the schema, which is the opposite of good compensation.

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 verb and resource: 'Find evidence for the same task across Claude Code, Codex, and Grok Build.' It clearly distinguishes this compare tool from siblings like worlds_get_cli_workflow (which likely returns a single workflow) and worlds_search_cli_reference (which searches reference material). The added detail of 'separately ranked groups with source dates and canonical links' further pinpoints the tool's unique scope.

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 by listing example use cases: 'configuring MCP, repository instructions, approvals, subagents, or headless CI.' The phrase 'Best for questions such as' signals when to prefer this tool. It doesn't explicitly say 'when not to use' or name alternatives, but the scope is well-implied. Sibling tool names like worlds_get_cli_command indicate a more narrow single-product lookup, but this exclusions aren't stated directly.

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