mz_dfs_read

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states that data is auto-sampled to max_points to avoid context overflow, explains how 'item' works for 1D results with examples, and notes that omitting 'item' defaults to the first item. It does not cover error handling or exact return structure, but the key behaviors are transparent.

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 appropriately concise and front-loaded with the purpose. The Args section is somewhat verbose but each sentence adds necessary detail, especially the item handling example. The structure is logical: purpose, behavior, then parameter explanations. No waste, though it could be tightened slightly.

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?

For a 5-parameter tool with no output schema and no annotations, the description is fairly complete. It covers all parameters, explains sampling behavior, and gives item examples. It does not describe the exact return format of statistics or series, nor error cases, but these are minor given the tool's scope and the absence of output schema. Overall, an agent can use this description effectively.

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 provides zero descriptions (0% coverage), so the description must fully explain parameters. It does: path is absolute path, item is explained with examples for both dfs and 1D cases, start/end are time strings with format example, and max_points is clearly the sampling limit. Every parameter is addressed with practical detail, going well beyond the schema's type/default information.

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 reads result data and returns statistics (max/min/mean) plus sampled series. The verb '读取' (read) is specific, and the resource is well-defined. It distinguishes itself from siblings like mz_dfs_info (metadata) and mz_dfs_plot (visualization) by focusing on actual data extraction.

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 explains what the tool does and how to use its parameters, but it provides no guidance on when to choose this tool over alternatives. It does not mention exclusions or conditions that would make another sibling more appropriate, such as when to use mz_dfs_plot instead for visualization or mz_dfs_info for metadata. The usage context is only implied by the tool's name and purpose.

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