ask-all

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

Annotations already declare readOnlyHint=true. Description adds 'advisory' and 'no cross-contamination' but does not disclose rate limits, cost, or error handling. With annotations present, this is acceptable but not rich.

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 concise sentences with no fluff. The first sentence covers core behavior, the second adds key parameter guidance.

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?

No output schema, 0% schema coverage, and 6 parameters. The description lacks details on result format, timeouts, error handling, and combined output, making it incomplete for a complex multi-model tool.

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 0%, so description must compensate. Only 'expert' is partially explained ('apply a persona'), leaving 5 other parameters (prompt, developerInstructions, cwd, reasoningEffort, files) undocumented.

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 fans out a question to multiple AI models in parallel for independent second opinions, distinguishing it from single-model siblings like ask-gpt and ask-gemini.

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 use for multi-model second opinions and mentions passing 'expert' for persona, but does not explicitly state when not to use or compare to other multi-model tools like consensus.

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