system_df

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

Even though annotations already mark the tool as readOnly and non-destructive, the description adds valuable behavioral context: it warns about a large payload on busy hosts and describes the return structure. This goes beyond the annotations and gives the agent a clear expectation of output size and format.

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 three concise sentences: the first defines the purpose, the second provides usage context and alternatives, and the third warns about payload size and return type. Every sentence adds unique value with no redundancy. It is well-structured and front-loaded.

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, read-only tool with no output schema, the description fully covers what an agent needs: what the tool does, when to use it, what to expect in the return, and a caveat about large payloads. It is complete in context with sibling tools and annotations.

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 the schema fully documents the input (empty object). The description correctly implies no parameters are needed, meeting the baseline of 4 for no-parameter tools. No additional parameter explanation is required.

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 states a specific verb and resource: 'Summarize Docker disk usage' with breakdown of layer storage and per-object sizes for images, containers, volumes, and build cache. It also clarifies the equivalent docker command (`docker system df`). This clearly distinguishes it from sibling tools like `system_info` and `buildx_du`.

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 explicitly tells the agent when to use this tool ('Use it to find what to reclaim before image_prune / container_prune / volume_prune / buildx_prune') and provides an alternative tool for different needs ('use system_info for daemon config and counts rather than sizes'). This is exemplary usage guidance.

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