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

With no annotations, the description carries full responsibility and excels: it discloses that no LLM is involved, lists the extraction_method enum, mentions type coercion, describes the output structure, and even states the cost per call. Limitations about nested schemas add critical expectations. This is far beyond typical descriptions.

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 dense but every sentence provides distinct value: endpoint, input, output, methods, behavior, limitations, and cost. It is structured logically with clear separators and avoids fluff or repetition of schema 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?

Despite lacking annotations and an output schema, the description compensates by fully specifying the output fields (success, extracted_data, extraction_method, validation_passed, error) and covering operational details (type coercion, flat-schema preference, cost). This is a complete picture for an agent to invoke the tool correctly.

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 detailed descriptions, so the baseline is 3. The description adds extra value by specifying that target_json_schema follows JSON Schema draft-07 and by framing the parameters as caller-supplied, which clarifies their role in the pipeline. This nuance earns a 4.

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 uses a specific verb ('extracts') and resource ('structured JSON from raw text') and clearly distinguishes the tool's function from the sibling tools, none of which suggest data extraction. The inclusion of the endpoint and input/output structure reinforces the purpose.

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 context on when to use the tool (for extracting structured data without an LLM) and notes a limitation ('Works best with flat schemas; deeply nested structures extract less reliably'), serving as a when-not. However, it does not explicitly name alternative tools or provide a clear 'use this instead of X' directive, so it falls 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.