smart_query

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

No annotations are provided, so the description carries the full burden. It discloses that the tool runs locally, has no API cost, saves over 80% tokens, and offers two response modes with token estimates. It does not cover state modification, permissions, or error handling, but for a read-only query tool, this is 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 fairly concise, using an emoji and bullet-like examples. It is front-loaded with purpose and benefits. A minor improvement could be clearer structuring, but overall it's effective.

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's simplicity (2 parameters, no output schema), the description covers purpose, usage, mode options, and examples. It is complete enough for an AI agent to understand and invoke the tool 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?

Schema description coverage is 100%. The description adds value by providing concrete examples of query content (error messages, function names, class names, questions) and explaining the mode options with token estimates (quick: ~500 tokens, detailed: ~1500 tokens), going beyond the schema's own descriptions.

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 as a smart query tool for error information, function names, or questions, returning concise answers. It somewhat distinguishes itself from siblings like search and browse by emphasizing local execution and token savings, but lacks explicit differentiation.

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 provides recommended use cases (error diagnosis, function lookup, quick learning) and mentions benefits (local, no API cost, token savings). However, it does not specify when not to use or compare to sibling tools like search or diagnose_error.

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