공시 원문 읽기

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

With no annotations, the description fully carries the burden. It discloses caching behavior (permanent, immutable), board_date extraction for reuse in check_disclosure_duty, and the HWP-only error case (body_unparsable + viewer URL). These go beyond schema basics and are highly actionable.

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?

One lead sentence plus four bullets, each covering a distinct behavioral aspect with no redundancy. It is slightly longer than absolute minimum but every sentence earns its place, and the bullet structure improves scannability.

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?

No output schema, so the description covers return behavior: markdown tables, truncation via max_chars, and the HWP error path. It doesn't enumerate every possible return field, but for a raw-text-read tool this is sufficient. The board_date integration note adds extra practical value.

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%, so baseline is 3. The description adds rationale for force_refresh (disclosures are immutable) and reinforces the format distinction (markdown preserves tables), but most parameter meaning is already in the schema.

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 it returns the full disclosure text as markdown-preserving tables. The verb '읽기' (read) with resource '공시 원문' (disclosure full text) is specific and distinguishes it from siblings like search_disclosures (search) and find_precedents (precedents).

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?

It explicitly states when to use: '다른 회사의 기재 사례·문안을 참고하거나 공시 내용을 분석할 때 사용하세요' (use when referencing other companies' filing examples or analyzing disclosure content). It does not name explicit alternatives or exclusions, but the use context is clear.

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