chat

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool 'Returns streaming response with AI insights, tool calls, and data visualizations,' which adds useful context about output behavior. However, it omits details like rate limits, error handling, or authentication requirements, leaving gaps in transparency.

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 appropriately sized with three sentences that are front-loaded with core functionality. Each sentence adds value: the first states the purpose, the second describes the response format, and the third clarifies user support. There is no wasted text, though it could be slightly more streamlined.

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 the complexity of a 5-parameter tool with no annotations and no output schema, the description is moderately complete. It covers the tool's purpose and user types but lacks details on error cases, performance expectations, or example outputs. This leaves room for improvement in guiding an AI agent effectively.

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 input schema has 100% description coverage, providing detailed documentation for all parameters. The description adds minimal semantic value beyond the schema, only implying user type distinctions (external vs internal). This meets the baseline for high schema coverage but doesn't significantly enhance parameter understanding.

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 purpose: 'Chat with Cube AI agent for analytics and data exploration.' It specifies the verb ('Chat'), resource ('Cube AI agent'), and domain ('analytics and data exploration'). However, since there are no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, preventing a perfect score.

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 implied usage guidance by mentioning support for 'both external users (with custom attributes) and internal Cube users (with existing permissions),' which hints at when to use externalId vs internalId. However, it lacks explicit instructions on when to choose this tool over other analytics methods or clear exclusions, resulting in a moderate score.

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