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

Beyond the readOnly/idempotent/destructive annotations, the description discloses token-length behavior (>=3 chars FTS vs 1-2 char LIKE), ordering behavior, item_type filtering, snippet truncation semantics, unique_key formatting, and the empty-query error contract. This is substantial behavioral detail far beyond what the annotations provide.

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 organized into clear sections ([Purpose], [Usage], [Response], [Rules]) and every sentence carries actionable information. It is reasonably long only because it documents edge cases and integration behavior, which is necessary for correct invocation.

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 search tool with three parameters and no output schema, the description covers input semantics, output shape, error behavior, filtering constraints, and downstream usage via get_paragraph. The response structure is explicitly documented, so an agent has enough information to call the tool and interpret its results correctly.

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?

Although the input schema already documents all parameters, the description adds operational meaning: query tokens are space-separated with different matching behavior by token length, std_num constrains to a specific standard such as 1116, and limit is shown with example values like 30. The unique_key format and snippet behavior also clarify how results relate to parameters.

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 begins with a specific verb and resource: full-text search across K-IFRS, general accounting standards, audit standards, internal accounting control, and ESG(KSSB) standard text. It clearly distinguishes itself from get_paragraph (which retrieves full metadata/context by unique_key) and from the Q&A-focused sibling search_qnas by stating that it searches 기준서 본문 only.

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 [Usage] section provides concrete query examples that map natural-language requests to parameter values, including query, std_num, and limit. It also explicitly tells the agent to pass a hit's unique_key to get_paragraph for full text, and the [Rules] section states exclusions such as title rows being filtered out.

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