Installed software

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

Annotations already mark the tool as read-only and non-destructive. The description adds behavioral details: default sort by size, ability to sort by recent, and filtering by name. No contradictions.

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?

Two sentences pack all essential information: what the tool does, default behavior, use cases, and filterability. No redundancy.

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 simple read-only tool with full schema coverage, the description is complete. It explains the data returned (size, publisher, install date), sorting options, and filtering. No output schema needed as return format is implicit.

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?

Schema coverage is 100%, so baseline is 3. The description adds real-world meaning to parameters, e.g., 'sorted by size by default' reinforces the sort_by parameter and 'filterable by name' clarifies the filter parameter. The use case hints provide additional context.

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 lists installed programs with specific attributes (size, publisher, install date). It provides usage context like 'great for finding bloat' and distinguishes itself from sibling tools implicitly through its focused data set.

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 gives clear usage scenarios (finding bloat, diagnosing slowdowns after installation). It mentions filterability and sort options, but does not explicitly exclude or recommend alternatives, though sibling tools are present.

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