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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds context the annotations do not: that matches are table-level facts and that results include the associated amounts, YoY change and share of total. It stops short of describing result ordering or what happens when a name matches multiple topics.

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 core capability and the field enumeration are front-loaded, and the closing sentence about search_filings is a well-placed routing note. The bracketed product tag ("[국내 주식 + 채권 MCP FinQuest]") and the long mid-sentence enumeration add length without adding decision-relevant information, keeping it short of a 5.

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?

There is no output schema, and the description compensates by stating the return payload (values with amount, YoY and share of total). With six parameters at 83% schema coverage and full annotation coverage, nothing an agent needs in order to select and call this tool is missing.

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 high (83%), so the baseline is 3, but the description adds real meaning by mapping example queries onto parameter concepts — a customer name maps to the 고객 dimension, a material to 원재료 — which hints at how `topic` and `item` narrow results. The `market`, `min_mcap` and `limit` filters are not discussed in the description, but their schema entries carry the semantics.

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 names a specific verb and resource ("사업보고서 표 내용으로 회사 찾기") and then enumerates exactly which table fields are searched (부문·품목·브랜드·고객·원재료·지역·수주 프로젝트·발주처·매입처·연구과제). It also states what comes back (금액·전년 대비·합계 대비 비중), so an agent can distinguish it from generic company or filing search without opening the schema.

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 supplies concrete example questions that map to distinct query types ('HBM 매출 있는 회사', '현대자동차에 납품하는 회사', '리튬을 사는 회사', '사우디 수주', '포스코에서 매입') and explicitly routes the excluded case — sentence/description search — to search_filings. Both when-to-use and when-not-to-use are covered.

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