查看数据文件结构：列名/类型/行数/每列非空数 + 前 N 行预览。免费(0 credit)。
支持 CSV/TSV/JSON/NDJSON/Parquet。取数(data_query)前先调它看清有哪些列、哪些是数值。
| Name | Required | Description | Default |
|---|---|---|---|
| fmt | No | 格式 csv/tsv/json/ndjson/parquet；缺省按内容自动识别 | |
| file_id | No | 已上传到文件中转站的数据文件 ID（与 data_base64 二选一） | |
| data_base64 | No | 数据文件内容 base64（与 file_id 二选一，适合未上传的临时数据） | |
| preview_rows | No | 预览前 N 行 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool is free (0 credit), supports specific formats, and describes typical use. This adds useful behavioral context beyond the schema, such as cost implications and format support.
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 concise and well-structured: the first sentence states the core function, the second adds format support and usage guidance. No redundant or filler text; every sentence earns its place.
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?
With no output schema, the description compensates by enumerating what the tool returns (column names/types/row count/non-null counts + preview). It also covers supported formats, cost, and context of use. It could mention that preview_rows controls preview length, but that is already in the schema. Overall, it provides sufficient context for a tool of this complexity.
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 schema already fully documents all parameters (fmt, file_id, data_base64, preview_rows) including their meanings and constraints. The description adds a small amount of context (e.g., data_base64 for temporary data), but since the schema does the heavy lifting, a 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 clearly states the purpose: inspect data file structure (column names/types/row count/non-null counts) and preview first N rows. It explicitly contrasts with sibling tool data_query by advising to call it before querying, thus distinguishing itself.
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 description provides clear usage context: call before data_query to understand available columns and numeric fields. It implies this tool is for inspection, not querying. It doesn't explicitly discuss when not to use, but the guidance 'before data_query' is actionable and sufficient for this simple tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool targets a distinct data operation (clean, convert, inspect, join, profile, query). There is minor overlap between data_inspect and data_profile (both provide column types and null information), but descriptions and names make the intended use clear.
All tool names follow a consistent 'data_' prefix plus a concise verb (clean, convert, inspect, join, profile, query). The pattern is uniform and predictable.
Six tools is well-scoped for a data processing toolbox. Each tool covers a core data wrangling need, and the count is neither sparse nor overwhelming.
The toolset covers the main data lifecycle: cleaning, format conversion, structural inspection, profiling, joining, and querying. No obvious critical operations are missing for common data analysis workflows.