Diagnose LLM Config

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

Annotations already cover the safety profile (readOnly, non-destructive, openWorld) and the description adds real value beyond them: potential charges, that synthetic data is sent to the external provider, that board contents are not read, and that semantic/schema failures are reported. It does not contradict the annotations — outbound provider calls are consistent with openWorldHint and the non-idempotent hint aligns with per-call charges.

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?

Four short sentences, front-loaded with what it does, followed by side effects, scope boundary, and timing. Dense but every sentence carries information; minor compression possible in the failure-reporting clause.

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 diagnostic with an output schema (so return values need no explanation), the description covers purpose, cost side effects, external data transmission, scope exclusion, and when to invoke it. Nothing material is missing.

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 takes zero parameters, so per the rubric the baseline is 4; there is nothing for the description to disambiguate.

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?

States a specific verb (diagnose/test) and resource (configured text and vision models) plus the exact scope: three small synthetic JSON challenges. It is unmistakably distinct from the board-oriented siblings like ingest_board and get_board_context.

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 names the trigger condition: 'Use after changing model/provider settings.' No alternative tool is named and no when-not-to-use is given, but the diagnostic framing makes the context clear.

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