extract_json_from_text

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

Without annotations, the description must fully disclose behavior. It details input processing (stripping fences, comments) and output (first parseable value and start position). However, it omits failure behavior when no JSON is found, which is a notable gap for a parsing 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?

Three sentences, each serving a distinct purpose: stating the action, listing transformations, and describing the return. Front-loaded with the main verb, no wasted words.

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?

With no output schema, the description partially covers the output (first parseable value and start position). It lacks detail on edge cases (e.g., multiple JSON objects, non-JSON input), which reduces completeness for a utility tool.

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% for the single 'text' parameter, and the description adds value by explaining how the text is preprocessed (stripping fences, comments), which goes beyond the schema's simple 'contains a JSON value' 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 explicitly states the tool's purpose: to extract and parse a JSON value from messy LLM output, detailing specific transformations (stripping ```json fences, prose, comments). It clearly distinguishes from sibling tools 'parse_partial_json' and 'validate_json' by focusing on messy text rather than partial or valid JSON.

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 for cleaning LLM output to extract JSON, but does not explicitly state when not to use it or provide direct comparisons to siblings. The context and examples (fences, comments) give practical guidance, though more explicit exclusions would improve clarity.

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