read_source_data

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

Even with readOnlyHint=true, idempotentHint=true, and destructiveHint=false in annotations, the description adds meaningful behavioral context: it stamps the baseline week onto rows, and every row carries file/sheet/row coordinates to support later issue pointing. It also discloses the output contents (row counts, per-week task counts, unsubmitted task codes), going well beyond what the annotations alone convey.

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 with clear sections (main description, Returns, Examples) and front-loads the core purpose. There is slight redundancy between '그 규모를 돌려줍니다' in the first line and the 'Returns' details, but overall every section earns its place and no unnecessary content is present.

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 the rich input schema, full parameter documentation, annotations, output schema, and explicit use/not-use examples, the description is complete for an agent to select and invoke the tool correctly. Nothing essential about purpose, behavior, or when to use it 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 description coverage is 100%, so the input schema already documents all three parameters (전주차, 기준주차, 원본디렉터리) with defaults and explanations. The description does not add additional parameter-level meaning, so the baseline score of 3 is appropriate.

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+resource statement: '원본 엑셀을 읽어 정규화 사실표로 만들고 그 규모를 돌려줍니다' (reads the original Excel, builds a normalized fact table, and returns its scale). It also distinguishes itself from sibling tools with the explicit not-used example routing to compute_baseline_findings, so an agent can tell it apart even without checking 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?

The 'Examples' section explicitly states when to use the tool ('원본에 몇 개 과제가 들어와 있나요?' / '누가 안 냈나요?') and explicitly states when not to use it ('판정 5종이 필요할 때 → compute_baseline_findings'). This gives clear usage guidance and a named alternative, satisfying the highest bar for this dimension.

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