dm_datasets

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

No annotations are provided, so the description carries the full behavioral burden. It helpfully discloses the exact returned fields (id, name, shape, dimensions, dtype, type hint, per-dimension calibration), but it does not state read-only status, permissions, side effects, or error 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 a single compact sentence with a front-loaded verb and scope, followed by a tightly packed list of returned data fields. Every element describes what the tool yields, and the closing usage constraint is short and useful.

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?

Because no output schema exists, the description must convey the return shape, and it does so by enumerating the fields and type hints. For a zero-parameter read enumerator this is nearly complete, though it omits behavior for empty results or DM-not-open errors.

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 tool has zero parameters, so there is no parameter semantics to document in the description; the baseline for zero parameters is 4. The empty schema with additionalProperties false is consistent and needs no further parameter explanation.

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 uses a specific verb (枚举/enumerate) and resource (当前 DM 中所有图像数据集), and it lists the returned fields including type hints and calibration data. It does not explicitly contrast itself with siblings such as dm_metadata or dm_spectrum_info, so it falls short of the highest level.

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 phrase 分析前必查 explicitly tells the agent to call this tool before analysis, giving clear usage context. It does not provide when-not-to-use conditions or name alternative tools, so it is strong but not exhaustive.

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