funnels.list

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

Annotations only provide readOnlyHint: true. The description adds significant behavioral context: that the funnel is re-evaluated against current data, that cohort filters apply at session level (not retroactively per step), that definitions are immutable, and that a 404 is returned for non-existent names. It also clarifies that recomputation occurs for the requested period, which is not obvious from annotations alone.

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 a clear purpose statement, examples, and a limitations section. It is not overly verbose; every sentence serves a purpose. Examples are compact and illustrative. The front-loaded main functionality ensures quick comprehension.

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 (13 parameters, no required params, output schema exists), the description covers all essential aspects: main behavior with and without name, filtering capabilities, error condition (404), and immutability caveat. Since an output schema is present, the description need not detail return values. It provides enough context for an AI agent to correctly invoke the tool.

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?

Schema description coverage is 100% with detailed parameter descriptions. The description adds value by demonstrating how parameters combine in examples (e.g., pairing name with period, using device_type and channel). It also clarifies that cohort filters are optional segmentation tools. While the schema already documents each parameter, the description provides usage patterns that enhance understanding beyond isolated definitions.

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 that the tool retrieves and re-evaluates funnels, distinguishing between listing all funnels (without name) and retrieving a specific funnel (with name). It explicitly differentiates from funnels.create by noting that counts and conversion rates are recomputed. The verb 'retrieve and re-evaluate' is specific and the resource is clearly 'funnel'.

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 detailed when-to-use guidance: omitting name lists all funnels, providing name retrieves specific funnel. It explains cohort filters for segment comparison and gives concrete examples (e.g., 'paid traffic vs organic conversion – call twice'). It also lists limitations (404 if name not found, how to enumerate, immutability of definitions). This covers both when to use and when not to use alternatives.

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