list_data_science_projects

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

No annotations are provided, so the description carries full burden. It accurately describes the filtering logic and indicates a read-only operation, but does not address permissions, pagination, or side effects. For a simple list tool, this is minimally adequate.

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 sentence that directly states the tool's core function and filtering criteria. It is concise and front-loaded, with no unnecessary words.

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?

The tool is simple, and an output schema exists (not shown), so return format is covered. However, the lack of parameter documentation and missing guidance on when to use it versus siblings leaves the description incomplete for an agent to use it effectively.

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 only parameter 'cluster' is not explained in the description. With 0% schema description coverage, the agent has no information on its purpose, accepted values, or how it influences results. This is a critical gap that prevents correct usage.

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 OpenShift AI Data Science Projects and specifies the filtering criterion (namespaces with opendatahub.io/dashboard=true label). This distinguishes it from sibling tools like list_namespaces which lists all namespaces, providing a specific and actionable purpose.

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 implies usage for listing only labeled projects but does not explicitly provide when-to-use or when-not-to-use guidance. No alternatives are mentioned, so the agent must infer the appropriate context from the filter condition.

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