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

Goes well beyond the annotations: it discloses the pricing model ($0.003/call, first 25 free), that failed calls are free, and explicitly reconciles the non-obvious readOnlyHint=false by explaining the per-call charge. This is exactly the kind of context annotations cannot convey.

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?

Purpose and supported formats are front-loaded, followed by usage, example, and pricing. Slightly dense with the pricing sentence, but each sentence carries distinct information the agent needs.

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 two-parameter pure transform tool with no output schema, the description covers inputs, cost, and failure semantics well. It never states the return shape (a converted string), which is implied but not explicit, leaving a minor gap.

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 100%, so the schema already documents both parameters, setting a baseline of 3. The description adds incremental value by showing mixed-input tolerance ('userID_value') and a concrete call example (text=user profile id, to=camel) that maps values to both parameters.

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 (Convert) and resource (text or identifiers) and enumerates all eight target formats, so an agent immediately understands scope. It is clearly distinguishable from siblings like slug or text_stats, which do different transformations.

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 use case ('Use when renaming fields or generating code identifiers'), which gives clear context for selection. It stops short of naming alternatives or exclusions (e.g., when to prefer slug instead), so it does not reach the top band.

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