list_projects

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

Annotations already declare readOnlyHint=true, and the description adds behavioral context: it explains how cluster_core projects are included, how visibility hints work, and that unreadable components are reported as unchecked, never guessed. This goes beyond the annotation by detailing edge-case 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 dense but information-rich, covering all key aspects in a single paragraph. It is front-loaded with the core purpose and then details edge cases. Slightly long but every sentence adds value.

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?

Given the tool has no parameters and no output schema, the description fully compensates by explaining the return fields, the meaning of cluster_core, visibility hints, and the recommended usage pattern. It is complete for an agent to use correctly.

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, and the schema coverage is 100% (no params). The description explains the output fields in detail, which is valuable since there is no output schema. It compensates for the lack of structured output documentation.

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 all projects tracked by ratatosk, specifying the fields returned (slug, name, tier, category, analyzed_releases, image_aliases, cluster_core) and the purpose of each. It distinguishes itself from siblings by emphasizing it is the canonical source for slugs and should be called first when unsure.

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 instructs to call this tool FIRST when unsure of a slug, and warns against guessing (a wrong slug shows up as tracked:false in check_stack). This provides clear when-to-use guidance and differentiates from sibling tools like check_stack.

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