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

Annotations already cover read-only, non-destructive and closed-world, so the description doesn't need to restate safety. It adds real value beyond them by disclosing the return contents (shape, types, missing counts) and by asserting that computation runs on a deterministic engine rather than an LLM, which is a meaningful trust signal the agent cannot get from structured fields.

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?

Front-loaded with the action and use case, then the return values, then an execution note. The bracketed heading duplicates the annotation title, which is a small waste, but the rest of the text is tight.

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 does useful work by listing the returned fields. However, for a parser it omits edge-case behavior (malformed rows, delimiter inference, empty input, size limits), and csv_data being optional despite being the only input is left unexplained.

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?

With one parameter at 100% schema coverage, the schema already explains 'csv_data'. The description adds no detail on delimiter, header handling, encoding, or size limits, so baseline 3 applies.

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?

States a specific verb and resource (parse a pasted CSV/table into column types) and enumerates what comes back: row/column counts, column types, missingness. It does not name or contrast with the closest siblings (act_data_quick_stats, act_file_columns), so it falls short of a 5.

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?

"用户贴了一段 CSV/表格，要先看数据长什么样" implies a first-look, pre-analysis use case, which is genuine usage context. But there is no explicit when-not, no prerequisite statement, and no pointer to the similar sibling tools that also summarize tabular data.

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