analyze_text_for_bias

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

No annotations are provided, so the description carries the transparency burden. It indicates a read-only analysis action ('analyzes', 'suggests') and no side effects are implied, but it does not disclose output format, limitations, or any processing details beyond the basic function.

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 a single, front-loaded sentence with no redundant wording. Every element ('analyzes', 'given text', 'suggests cognitive biases', 'core diagnostic engine') adds meaningful information.

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 simple one-parameter tool with no output schema, the description sufficiently explains the tool's purpose and provides input examples. It could be more detailed about the exact return format (e.g., list of biases with explanations), but the phrase 'suggests which cognitive biases might be present' gives a reasonable indication.

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 already provides a clear description of the 'text' parameter (100% coverage), and the tool description adds semantic depth by offering examples of what constitutes valid input (argument, decision, belief). This goes beyond the schema's generic phrasing.

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 ('analyzes') and resource ('text') and clearly states the output ('suggests which cognitive biases might be present'). It distinguishes itself from sibling tools like get_supported_biases (which lists biases) and get_test_recommendation (which recommends tests) by framing itself as the core diagnostic engine.

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 this is the primary tool for bias analysis ('core diagnostic engine function') and gives examples of appropriate inputs (argument, decision, belief). However, it does not explicitly contrast with sibling tools or state when to use it instead of get_supported_biases or get_test_recommendation.

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