extract_resume_data

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

There are no annotations, so the description must convey behavioral traits. It states this is a validation/assertion tool, implying a read-only, check-only operation. However, it doesn't disclose what happens on invalid input (e.g., error messages, return format), which is a significant gap for a validation 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?

The description is two sentences, front-loading the key purpose ('validating') and providing a clear action directive. Every word earns its place, with no redundant filler or fluff.

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?

There is no output schema and no annotations, so the description needs to explain what the tool returns (e.g., a boolean, detailed errors, or list of validation issues). It also doesn't mention side effects or whether it modifies data. This lack of return-value and side-effect documentation makes the description incomplete for an agent to fully understand the tool's behavior.

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 schema is highly detailed with a single 'cv' parameter containing many nested required fields. The description adds context by explaining that the JSON comes from AI-extracted resume/LinkedIn text and must conform to a strict format, but it doesn't mention the parameter name or any property-level details. Since schema coverage is excellent, the description adds minimal semantic value beyond the schema itself.

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: validating AI-extracted resume/LinkedIn text into a strict CV JSON format. It uses a specific verb ('validating') and resource ('CV JSON format'), and distinguishes it from siblings like generate_cv_pdf by framing it as a pre-generation validation step.

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 explicitly says when to use the tool ('Pass your organically constructed JSON against this to assert validity before generating PDFs or Profiles'), which implies a clear workflow context. It doesn't enumerate alternatives, but the sibling names and 'before generating' make the usage timing unambiguous.

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