get_microconversions

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

No annotations are provided, so the description carries full burden. It mentions retrieving data but doesn't disclose behavioral traits such as rate limits, authentication needs (implied by account_id), pagination behavior (hinted by limit/skip parameters but not explained), or what happens if filters yield no results. The example question adds some context but lacks operational details.

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 two sentences: the first states the purpose and source, the second provides a concrete usage example. It's front-loaded with key information and has zero waste, making it easy to scan and understand quickly.

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 (9 parameters, no annotations, no output schema), the description is adequate but has clear gaps. It covers the purpose and gives an example, but lacks details on behavioral aspects like error handling, response format, or how filters interact. Without annotations or output schema, more context on what the tool returns would be helpful.

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%, so the schema fully documents all 9 parameters. The description adds minimal value beyond the schema by mentioning examples like 'add-to-cart' and 'newsletter-signup' for the label parameter, but doesn't provide additional semantics for other parameters. Baseline 3 is appropriate as the schema does the heavy lifting.

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 'microconversion data (add-to-cart, signups, etc.) from Sealmetrics' with a specific example question. It distinguishes from siblings like 'get_conversions' by focusing on microconversions, though it doesn't explicitly contrast with all siblings. The verb 'Get' is clear but could be more specific like 'Retrieve' or 'Fetch'.

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 implies usage through the example question 'How many add to carts from Google Ads yesterday?' which suggests filtering by label and date. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_conversions' or 'get_funnel_data', nor does it provide exclusions or prerequisites. The guidance is contextual but not comprehensive.

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