export_data

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

The description states the tool returns the full contents of the selected table, which is the primary behavior. However, with no annotations provided, the description carries the full burden of transparency and does not disclose potential limitations like rate limits, data volume handling, or whether the operation is read-only. More detail on response size or pagination would be beneficial.

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 front-loaded with the main purpose and use cases, followed by an organized list of tables. It is concise given the number of tables, though the list could potentially be shortened if some tables are less relevant. Overall, it is well-structured and efficient.

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 export tool with no output schema, the description covers the purpose, usage, and available tables adequately. It does not detail the output format (though JSON is mentioned) or handling of large datasets, but for most use cases, the description provides sufficient context. Slight improvement could mention if the export is per-page or all-in-one.

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 input schema has 100% coverage with the 'table' parameter described via an enum and brief description. The description adds value by grouping the enum values into categories (Core, Activities, Media, etc.), providing semantic context beyond the raw list, which aids an AI agent in selecting the correct table.

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 is for bulk exporting an entire data table as JSON, listing specific use cases (reporting, backup, migration, analytics) and providing an extensive categorized list of available tables. This distinguishes it well from sibling tools that focus on specific CRUD operations or searches.

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 explicitly mentions use cases and lists available tables, giving clear context for when to use the tool. However, it does not provide explicit guidance on when not to use it or how it compares to alternatives like search or create tools, which would be helpful for an AI agent deciding between tools.

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