Recommend next evidence

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds that this is an advisory recommendation with expected score impact and an educational explanation of what data improves output. It does not mention permissions, rate limits, or clarifying details about what exactly is returned beyond the phrase 'expected score impact'.

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 two short sentences and front-loads the core action: recommend the smallest next source with expected impact. 'Teaches what data improves output' is slightly redundant with the quality-improvement framing but still adds a useful orientation for the agent.

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 read-only recommendation tool with no output schema, the description gives the gist of the output ('expected score impact') but does not describe the return shape. It also omits when to choose this tool over sibling diagnostic tools, leaving a meaningful gap for selection.

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 description coverage is 100%, so the schema fully documents goal, proof, and workspace. The description does not add parameter-level detail beyond the schema, which is acceptable given the high coverage baseline.

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 (recommend) and a specific resource (the smallest next source to improve generated-test quality), plus that it includes expected score impact. It is clearly distinct from analyze_sources or find_test_gaps, but it never names those siblings to reinforce the distinction.

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?

Usage is only implied: an agent can infer this is for deciding what data to add when generated-test quality needs improvement. There is no explicit when-to-use, when-not-to-use, or comparison against alternatives such as analyze_sources or find_test_gaps.

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