RFM Analysis

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, which is reinforced by the description's focus on analysis and output generation. The description adds context about data source options (auto, hubspot, sample) and return structure, which further clarifies behavior. No contradictions with 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 structured with a clear summary, output explanation, and parameter details. It is front-loaded with the tool's purpose and avoids redundancy, though the parameter details could be condensed slightly.

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 existence of an output schema (though not detailed here), the description covers inputs, outputs, and key behavioral aspects. It lacks error handling or edge-case guidance but is sufficient for typical usage.

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 provides only parameter names with defaults (0% description coverage). The description compensates fully by explaining each parameter's meaning, options (e.g., 'auto', 'hubspot', 'sample' for source), and purpose, which is critical for correct usage.

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 runs RFM analysis on client data, detailing scoring, segmentation, ICP extraction, and win/loss detection. This differentiates it from sibling tools like analyze_engine or qualify, which serve different purposes.

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 explains what the tool does but does not explicitly state when to use it instead of alternatives. Users can infer it is for customer purchase behavior analysis, but no direct guidance on context or exclusions is provided.

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