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

Annotations declare readOnlyHint, idempotentHint, and destructiveHint, and the description adds substantial context: it returns excerpts and URLs, merges multiple hits from the same article into one, and has a fallback behavior of stating absence instead of guessing. No contradiction with 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 well-structured: purpose first, then usage examples, then limitations. While somewhat long, every sentence adds value and it is not redundant. Front-loading is effective.

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 adequately explains the return (excerpts and article URLs) and covers edge cases (no results, merging behavior). For a search tool with two params, this is complete and anticipates agent questions.

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 adds meaningful guidance: query format examples (service type + specific aspect) and clarifies that top_k is a chunk count that may result in fewer articles due to merging. This goes beyond the schema's basic parameter descriptions.

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 clearly states the tool searches articles on a specific site (fukushi.ai), returns excerpts and URLs, and is verified against primary sources. It distinguishes itself from the sibling get_article by focusing on search rather than retrieval, and gives specific examples of queries.

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?

Explicitly instructs when to use this tool: when asked about services' systems, remuneration, add-ons, standards, or practical procedures, and provides concrete example questions. It also states when not to use it (if no article exists, tell the user rather than guessing) and notes scope limitations.

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