neural_insights

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

A dedicated section covers behavioral aspects comprehensively: read-only, stateless, idempotent, no side effects, authentication requirements, rate limits, error handling, and data privacy. This fully compensates for the lack of annotations.

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 well-structured with clear sections, but it is somewhat verbose and contains minor repetition (e.g., read-only mentioned twice). Most sentences add value, making it effective despite length.

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?

Given one optional parameter and no output schema, the description covers purpose, usage, behavior, and parameter context adequately. However, it omits details about the exact format or structure of the returned insights, which could aid tool invocation.

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 only parameter, 'api_key', is explained in the description: optional for basic usage, required for higher tiers. This adds meaning beyond the schema's default, though the description doesn't detail the key format or validation.

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 retrieves 'aggregate learning insights' from a neural compliance model, specifying topics like training history and risk patterns. While it doesn't explicitly differentiate from siblings like 'predict_risk_neural', the purpose is specific and actionable.

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 includes explicit 'When to use' and 'When NOT to use' sections, detailing appropriate scenarios such as compliance auditing and gap analysis, and cautioning against using it as legal advice. This provides clear decision criteria for the agent.

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