analyze_run

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

With no annotations, the description carries the burden and does disclose what analyses are performed (anti-patterns, numeric metrics, plan analysis, sizing), which is useful behavioral context. However, it never states that the tool is read-only/non-mutating, what happens if inputs are partial or mismatched, or any prerequisite relationships between inputs — significant gaps for an 8-parameter tool with zero annotation coverage.

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 with a colon-delimited output list — no filler and no redundancy. It is dense but every clause earns its place; slight deduction for packing multiple analyses into one long sentence rather than structuring them.

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 analysis tool with 8 inputs including nested objects, 0% schema description coverage and no annotations, the description is too thin. It doesn't explain the expected shape of the required 'run' object or how plan/tasks/stages/spark_conf/stage_plans/cluster_specs feed the analyses. The output schema covers return values, but input-side completeness is lacking.

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 description must compensate, but it only loosely alludes to four inputs (task metrics, Spark plans, executors, cluster specs) without using parameter names or formats. It omits the required 'run' object entirely and never mentions stages, spark_conf, or stage_plans — a major gap for 8 parameters.

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 (Analyze) and resource (a Databricks job run), and enumerates the analyses produced: anti-patterns, shuffle/skew/spill metrics, plan analysis and cluster sizing. It implicitly differentiates from analyze_run_by_id via 'from supplied task metrics...' (i.e., input supplied rather than fetched), but never names any sibling explicitly.

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 statement of when to use this tool versus analyze_run_by_id, analyze_stage or diagnose_job, and no prerequisites or exclusion conditions. The only guidance is the implicit 'from supplied ... ' phrase, leaving the agent to infer that this is for pre-collected data.

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