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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds behavioral context beyond annotations by specifying that it extracts the *first* valid JSON and handles markdown-fenced and inline JSON, which is important for invocation. No contradiction with annotations found.

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 concise and well-structured: the first sentence states the core function, the second covers input format handling, and the third gives explicit usage context. Every sentence earns its place with no redundancy or unnecessary detail.

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 tool with one parameter, complete schema coverage, an output schema, and thorough annotations, the description provides ample context. It covers what the tool does, when to use it, and what input formats are acceptable, making it easy for an agent to select and invoke the tool correctly. The minor absence of edge-case behavior (e.g., no JSON found) is negligible given the output schema.

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?

The single parameter 'input' is well-defined in the schema, with high coverage. The description enriches the parameter meaning by clarifying the input type ('chaotic LLM output', 'surrounded by markdown fences, prose, or explanatory text') and that the extraction targets the first valid JSON—details that go beyond the schema 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 tool's function: extracting the first valid JSON object/array from chaotic LLM output. It specifies the resource (JSON in text) and the action (extract), and distinguishes itself from sibling tools like extract_json_path and format_json by focusing on embedded data surrounded by prose or markdown fences.

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 an explicit when-to-use scenario: 'Call this whenever an LLM returns structured data mixed with explanation text instead of raw JSON.' This gives clear practical guidance. However, it does not name alternatives or explicitly state when not to use the tool, so it misses the upper boundary of a 5.

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