read_ec_results

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

With no annotations, the description carries the full burden. It discloses important behavioral traits: returns 409 for incomplete runs, 422 for invalid datasets, and explains the semantics of each dataset type. It does not mention pagination or rate limits, but the schema covers pagination fields and output schema is present. Overall good behavioral disclosure.

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 concise and well-structured: three sentences front-load the core purpose, then provide critical usage nuances and error conditions. Each sentence adds value; no fluff or redundancy. Formatting with bold and inline code improves readability without adding length.

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 presence of an output schema and 100% schema coverage, the description need not explain return values or all parameters. It covers the essential operational context: preconditions (completed run, dataset availability check), error codes, and dataset semantics. This is sufficient for an agent to correctly select and 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 coverage is 100%, so the baseline is 3. The description adds meaningful context beyond the schema by explaining the conditions for dataset=owned (requires product_id in start_run) and dataset=competitor (requires competitor_asins, one row per ASIN), and clarifies that other datasets return 422. This is more than a simple restatement.

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 function with a specific verb ('Fetches') and resource ('daily EC performance of a completed run'). It distinguishes itself from siblings like read_social_results by focusing on EC data and from read_result_summary by being a data retrieval tool with explicit guidance to use the summary for checking dataset availability.

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 clear usage context by explaining when dataset=owned vs competitor is appropriate, and explicitly tells the user to check dataset availability via read_result_summary. It lacks explicit statements about when not to use this tool or direct alternatives, but the guidance is strong and contextually complete.

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