bubble_field_usage

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

Annotations already declare readOnly, idempotent, and non-destructive behavior, lowering the bar. The description adds valuable context by stating it samples records, calculates rates, and uses a specific 5% threshold for dead fields. It does not discuss performance or sampling methodology, but for a read-only analysis tool, this is sufficient transparency.

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?

Two compact sentences front-load the action and key result. Every word earns its place, with no fluff or repetition of schema fields. The threshold detail is useful and efficiently stated.

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 tool with two parameters and no output schema, the description covers the purpose, method (sampling), and a specific output characteristic (dead fields with <5% population). It omits what the exact return format looks like, but given the simplicity and read-only nature, it is nearly complete. Could mention if it returns per-field percentages, but not essential.

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% with both dataType and sample_size already described in the schema. The description adds no new parameter details beyond what the schema provides, so the baseline of 3 applies. It does indirectly reference dataType as 'Bubble data type' but does not enhance the schema's clarity.

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 purpose: sampling records, calculating field population rates, and identifying dead fields. This specific verb+resource combination distinguishes it from siblings like bubble_schema_summary or bubble_find_orphans, which focus on schema structure or orphan records rather than field usage.

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 when to use this tool: when you need to assess field population and find dead fields. It does not explicitly mention alternatives or exclusions, but the context is clear and distinct from sibling tools, so it provides adequate guidance without needing to name alternatives.

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