extract_json

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

With no annotations provided, the description carries full burden. It transparently explains the fallback strategy: tries whole text, then fenced block, then largest balanced substring, and returns the parsed value plus which strategy succeeded. This is sufficiently detailed for a read-only cleanup tool.

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, front-loaded with purpose and followed by strategy. Every sentence adds value with no wasted words. Easy for an agent to parse quickly.

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 (1 param, no nested objects, no output schema), the description is fairly complete. It explains the return value (parsed value plus strategy). The absence of an output schema is mitigated by the description's mention of the return format.

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% with a clear parameter description. The description adds value by explaining how the text parameter is processed (the extraction strategy), which goes beyond the schema's basic description.

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 purpose: 'Pull a JSON value out of messy LLM output.' It specifies the verb and resource, and the context of use (LLM output). It distinguishes itself from siblings by focusing on JSON extraction, while siblings deal with retry and validation.

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 usage when extracting JSON from messy LLM output, but does not explicitly state when not to use it or mention alternative tools. No exclusion criteria or when-to-use guidance beyond the implied context.

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