fetch_job_run_context

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

No annotations are provided, so the description carries the full burden. It does disclose what the response contains (metadata, metrics, SQL, Spark plan), which is useful behavioral context, but says nothing about permissions, payload size/latency, or whether the call is strictly read-only and side-effect free.

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?

A single front-loaded sentence that lists the returned artifacts without filler. Dense but every clause adds identifying detail.

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?

An output schema exists, so return values needn't be re-explained, but with zero parameter documentation and no annotation coverage the definition leaves an agent guessing about the two arguments and about when to prefer this over analyze_run_by_id or diagnose_job.

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%, and the description never mentions run_id or job_id at all. With no syntax, meaning, or explanation of how optional job_id relates to required run_id, the description fails to compensate for the coverage gap.

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?

States a specific verb ('Return') and resource ('full Databricks run context') and enumerates the payload: run metadata, task details, stage metrics, SQL text, Spark plan. This clearly separates it from the analyze_* siblings as a raw-context fetch, though it never names them.

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 phrase 'ready for AI analysis' faintly implies this is the fetch step preceding analyze_run or diagnose_job, but there is no explicit when-to-use, when-not, or alternative named among four closely related siblings.

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