Analyzes Databricks job run evidence to identify Spark performance bottlenecks across query plans, stages, executors, and clusters for natural-language agent workflows.
Alias for analyze_run intended for natural-language agent workflows.
| Name | Required | Description | Default |
|---|---|---|---|
| run | Yes | ||
| plan | No | ||
| tasks | No | ||
| stages | No | ||
| executors | No | ||
| spark_conf | No | ||
| stage_plans | No | ||
| cluster_specs | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral disclosure burden. It says nothing about whether this is read-only, what it returns, side effects, permissions, or how it differs behaviorally from analyze_run. Calling it an alias does not disclose 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 a single concise sentence that is front-loaded and contains no filler. However, its brevity leaves the definition under-specified rather than fully efficient given the tool's complexity.
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?
The tool has 8 parameters, nested objects, no annotations, and only an output schema. The description does not compensate for the missing schema descriptions or annotation coverage. It is far too incomplete for an agent to call the tool correctly without external knowledge.
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?
There are 8 parameters with 0% schema description coverage, and the description adds no parameter-level meaning. Required parameter 'run' and optional nested objects like tasks, stages, executors, and cluster_specs are unexplained. This is a severe 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?
The description identifies the tool as an alias for analyze_run, which gives some relational purpose but does not state what the tool actually does or what analyze_run does. An agent must already know the sibling's semantics to understand diagnose_job. It weakly distinguishes itself by framing as an alias, but the core purpose remains vague.
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?
It mentions 'natural-language agent workflows' as an intended context, but provides no explicit when-to-use rules, no when-not-to-use rules, and no comparison to analyze_run or other siblings. The agent is left to infer selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.