get_request_detail

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

With no annotations, the description carries the full burden. It does disclose the source table, the lookup key, and the returned content (query text and execution metrics), but it does not clarify read-only safety, permissions, or error/not-found behavior.

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 well-structured with a clear opening sentence and a parameter list. There is minor redundancy between 'completed query' and 'after the query completes,' but the text remains compact and easily scannable.

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 lookup tool with an output schema, the description is mostly complete: it identifies the source, the lookup key, the parameters, and the output type. It could be stronger by noting how to obtain distributed_statement_id or how this relates to request history listing tools.

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 description coverage is 0%, so the parameter documentation in the description is essential and effective. Each parameter gets a clear semantic definition, including accepted forms like 'name or GUID' for workspace and item, and 'GUID' for dist_statement_id.

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 states a specific action ('Look up a completed query') and a precise resource (queryinsights.exec_requests_history), and explains that it returns full query text and execution metrics. This distinguishes it clearly from list-oriented siblings like list_request_history and running-query tools.

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 the tool: after a query has completed and when a distributed_statement_id is available. However, it does not explicitly mention alternatives or state when not to use it.

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