jq_query

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

It discloses the important behavioral trait that no approval is needed, and mentions full jq syntax support. With no annotations, this is valuable. However, it does not cover error handling or output format details.

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: first states core function and approval note, second lists use cases and features. No redundant words, front-loaded with key information.

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 tool with 5 parameters, no annotations, and no output schema, the description covers the main purpose, common use cases, and the critical 'no approval' trait. It lacks details like default output format, but overall is fairly complete.

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 input schema already provides descriptions for all 5 parameters (100% coverage), so the baseline is 3. The tool description adds general usage context but no parameter-specific details beyond what the schema offers.

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 processes JSON using jq filter syntax and gives specific use cases. It stands out from sibling tools (mostly linting, building, testing) by focusing on data manipulation.

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 clear context for when to use (parsing API responses, extracting fields, etc.) but does not explicitly mention when not to use or suggest alternatives. However, given no similar siblings, it is adequate.

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