system_df

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description correctly adds extra behavioral context: it warns that the reply enumerates every object on the daemon and may be a large payload on busy hosts, and it describes the exact structure of the returned dictionary. This goes beyond the annotations and gives the agent a realistic expectation of the tool's output and performance.

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 compact and front-loaded: a clear purpose sentence, a usage guidance sentence, and a return-type hint. It includes the equivalency to `docker system df` and a performance caveat without extra fluff. Every sentence serves a purpose.

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 zero-parameter tool with no output schema, the description is fully self-contained: it explains what the tool measures, what the reply contains, and when to use it. The large-payload warning addresses a common pitfall, and the mention of sibling prune tools completes the workflow context. No gaps remain.

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 no parameters, so the standard baseline is 4. The description makes no parameter claims because none exist; it instead focuses on return structure, which is appropriate. There are no parameter semantics to clarify beyond what an empty schema implies.

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 opens with a specific verb ('Summarize') and resource ('Docker disk usage'), then enumerates exactly what is covered: layer storage plus per-object sizes for images, containers, volumes, and build cache. It also distinguishes itself from siblings by noting it is equivalent to `docker system df` and by contrasting with `system_info`.

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?

Explicitly states when to use this tool ('find what to reclaim before image_prune / container_prune / volume_prune / buildx_prune') and provides an alternative for different needs ('use system_info for daemon config and counts rather than sizes'). This gives the agent clear direction and avoids confusion with sibling tools.

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