generate_realtime_orders

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

With no annotations, the description carries the full burden and only discloses that it appends orders within a count range and year-window choices. It does not state side effects, persistence, idempotency, what 'as_job' does, or what the caller should expect afterward.

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 short sentences deliver the core action, count bounds, and year-window options without wasted words. It loses a point only because omitting as_job is a meaningful structural gap, not because of verbosity.

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 no annotations and no output schema, the description leaves out too much: return behavior, job semantics, prerequisite state, and how this append interacts with historical generation. An agent can make a default call but cannot fully reason about consequences.

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 description explains year_window values and indirectly maps '100-10000' to the count parameter, which helps. However, as_job is completely unexplained, and schema coverage is 0%, so the agent cannot derive its meaning from the schema either.

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 uses a specific verb ('Append'), identifies the resource ('new OLTP orders'), and gives a concrete count range, so an agent knows the core action. It does not explicitly contrast with generate_historical_oltp, but the 'realtime' name and year-window options make the intent reasonably distinguishable.

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?

There is no guidance about when to use this tool versus generate_historical_oltp, etl_star_cdc, or other siblings. The 'append' semantics hint at real-time ingestion, but the description leaves the selection criteria implicit.

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