minify_json_schema

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

With no annotations provided, the description carries the behavioral disclosure burden. It states what is stripped—verbose comments, redundant metadata, and whitespace—and also discloses the cost and benefit. It does not describe the exact output format or explain how the paymentSignature relates to the stated cost, but the core transformation behavior is transparent.

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 a single sentence, front-loaded with the action, followed by the benefit and cost. Every phrase earns its place, and there is no redundant or filler content.

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 relatively simple transformation tool with no output schema, the description covers the core input, behavior, and cost. The main gaps are that it does not explicitly state the return value is a minified JSON Schema string, and it leaves the paymentSignature requirement somewhat implied rather than explicit.

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 baseline is 3, but the description adds valuable meaning by clarifying that the payload should be a JSON Schema, not just any JSON string. The paymentSignature semantics remain only in the schema, but the schema already describes it adequately.

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 names a specific action ('Strips') and a specific resource ('large JSON Schemas'), making the tool's function clear. It does not explicitly contrast itself with sibling tools like clean_markdown_for_llm or llm_structured_json_schema_repair, so it falls short of full 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 clearly implies when to use the tool: when large JSON Schemas need to be compacted to reduce LLM context costs. It does not provide explicit when-not-to-use guidance or name alternative tools, so it stops short of a 5.

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