add_record

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

No annotations are provided, so the description carries the full burden for behavioral disclosure. It does reveal that the tool mutates the in-memory dataset and highlights the in-memory nature, but it does not mention return values, validation behavior, or error handling, which leaves some behavioral gaps.

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 a single, well-front-loaded sentence with no redundant information. Every word contributes to clarity, making it concise and to the point.

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 simple one-parameter tool, the description covers the essential aspects: what it does (adds a row), the data format (column name -> value), and the destination (in-memory dataset). It falls slightly short on edge cases, but the low complexity and presence of an output schema reduce the need for more detail.

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 schema provides only the parameter name 'record' with an object type and string values, with 0% description coverage. The description's 'column name -> value' explanation adds meaningful semantic guidance, effectively compensating for the sparse schema.

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 adds one row of scraped data to the in-memory dataset, using a specific verb ('Add') and resource ('row of scraped data'). It distinguishes itself from siblings like get_records, export_csv, and clear_records, making the purpose unambiguous.

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 the tool is for accumulating scraped rows during a scraping workflow, and the mention of 'in-memory dataset' clarifies that data is not persisted. No alternatives are needed since no other sibling tool performs this add function, and the guidance is clear enough for the simple operation.

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