把数据文件转成另一种格式（csv/json/ndjson/parquet/xlsx），产物存文件中转站返下载 URL（1 credit/次）。
典型：把大 CSV 转 parquet 便于后续分析、或把 JSON 日志转 xlsx 给人看。失败自动退款。
| Name | Required | Description | Default |
|---|---|---|---|
| to | Yes | 目标格式：csv / json / ndjson / parquet / xlsx | |
| fmt | No | 源格式 csv/tsv/json/ndjson/parquet；缺省自动识别 | |
| file_id | No | 已上传的数据文件 ID（与 data_base64 二选一） | |
| data_base64 | No | 数据文件内容 base64（与 file_id 二选一） |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full transparency burden. It discloses important behaviors: output stored in the transfer station with a download URL, one credit cost per call, and automatic refund on failure. This goes beyond schema details and helps the agent understand side effects and costs, though it doesn't mention file size limits or synchronous/asynchronous behavior.
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 front-loaded. The first sentence covers the core function and output; the second adds typical use cases and failure refund. Every sentence earns its place, with no redundant content or filler.
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 tool's medium complexity, no annotations, and no output schema, the description supplies key contextual elements: purpose, supported formats, typical scenarios, output mechanism (download URL), pricing, and failure policy. It doesn't spell out prerequisites like needing file_id or data_base64, but the schema covers that. Overall, it is complete enough for an agent to select and invoke the tool correctly.
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?
The schema covers 100% of the parameters with meaningful descriptions (e.g., fmt auto-detection, file_id/data_base64 mutually exclusive). The description only mentions target formats in the purpose line, adding no additional parameter nuance beyond what the schema already provides, 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 clearly states the tool's function with a specific verb ('把数据文件转成另一种格式') and lists supported target formats (csv/json/ndjson/parquet/xlsx). It also positions the output as a download URL from a file transfer station. This distinguishes it from sibling tools like data_clean or data_inspect, which focus on other data operations.
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 typical use cases ('把大 CSV 转 parquet 便于后续分析...把 JSON 日志转 xlsx 给人看'), giving contextual guidance on when to use the tool. However, it does not explicitly mention when not to use it or name alternative sibling tools, so it misses the full 'vs alternatives' guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.