seed_add_mock_data

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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals a key behavioral trait: 'Each row is treated as field values only,' which prevents misuse with relational data. However, it does not disclose other behavioral aspects such as whether the operation validates against schema, how it handles errors, or whether it overwrites existing data. For a simple mock data insertion, this is adequate but not exhaustive.

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 two sentences long, with the first sentence stating the primary purpose and the second providing a critical caveat and alternative. It is extremely concise, front-loaded with the action, and contains zero wasted words. Every sentence earns its place.

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 tool with only two parameters and no output schema, the description is largely complete. It covers the main behavior, the key constraint, and directs users to a related tool for a different use case. The only minor gap is not mentioning what the tool returns, but given the simplicity and the lack of an output schema, this is not a significant omission.

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% since both parameters have descriptions. The description adds semantic value beyond the schema by clarifying that each row is treated as field values only, which directly informs how to structure the 'rows' parameter. This goes beyond the bare schema descriptions and helps the agent understand the intended format.

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 action: 'Add simple mock rows to a Seed table.' It specifies the resource (Seed table) and the content (simple mock rows), and explicitly distinguishes from sibling seed_add_documents by noting that this tool does not handle relations. This makes the purpose unambiguous and differentiated.

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: use this tool for adding simple mock rows, and explicitly directs users to 'use seed_add_documents for relations' as an alternative. This gives a clear when-to-use and when-not-to-use reference, though it does not mention other potential alternatives like seed_add_relationship, which would make it more comprehensive.

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