list_feature_set_templates

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

The readOnlyHint annotation already declares this a safe read operation, and the description reinforces this with 'Browse.' It adds value by revealing the return content (template names, factors, conditioning_set_id) and its role as a prerequisite for another tool, which goes beyond the annotation.

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?

Two sentences: the first front-loads the core purpose, the second delivers a concrete output specification and downstream linkage. Every word earns its place, and no information is repeated from the schema or annotations.

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 zero-parameter, read-only tool with an output schema, the description is fully complete. It identifies the resource type, the exact outputs, and the intended consumer (analyze_quantitative), leaving no gaps for an agent to infer.

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 tool has zero parameters, so the schema provides no room for ambiguity. The description genuinely has nothing to add, and the baseline of 4 applies.

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 opens with 'Browse pre-built sets of market drivers' – a specific verb and resource that clearly distinguishes from sibling tools like list_feature_sets. Concrete examples (interest rates, volatility, commodities) and the mention of conditioning_set_id tie it directly to analyze_quantitative, making the purpose unmistakable.

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 states that the output includes 'conditioning_set_id needed by analyze_quantitative,' providing a clear downstream use case. However, it does not explicitly contrast with alternatives like list_feature_sets or state when NOT to use this tool, so exclusions are absent.

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