send_datahub_events

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

Annotations already indicate destructiveHint=true and readOnlyHint=false. The description adds valuable context: data becomes available in ~15 minutes and asks for confirmation before execution. No contradictions with annotations.

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?

Five well-organized sentences that front-load the purpose, then cover constraints, required fields, user confirmation, latency, and exclusions. No unnecessary words; each sentence adds value.

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 complex ingestion tool with a rich schema and no output schema, the description covers purpose, usage guidelines, constraints, latency, and alternatives. It does not describe the return value or error handling, but these are partially covered by the annotations and schema. Very high completeness overall.

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 schema already documents all parameters thoroughly. The description restates that provider and timestamp are required and dimensions/metrics optional, but adds no new semantic meaning beyond what's in the schema. Baseline 3 is appropriate.

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?

Clearly states the tool sends DataHub events for ingestion with a count range of 1–50,000 per call. It distinguishes itself from siblings by explicitly saying not to use for creating or viewing datasets, naming the correct alternatives.

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?

Gives explicit when to use ('when the user wants to send DataHub events'), advises to confirm event count and provider details before executing, and provides counterexamples ('Do NOT use for creating datasets...'). This leaves no ambiguity about appropriate use.

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