optimize_spark_performance

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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive. The description adds clarity by explaining that with provider='none' it returns raw fetched content, and otherwise returns a structured analysis. There is no contradiction with annotations, and the description provides useful context on what the tool does without describing side effects.

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-organized with a main purpose statement, an Args section listing each parameter with explanations, and a Returns section. It is thorough but not redundant, providing all necessary information without unnecessary 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?

The description explains the full behavior of the tool: what it analyzes, how it uses the parameters, and what it returns under different provider settings. Although an explicit output schema is not shown, the description clearly describes the two possible return types (structured analysis or raw content), making it complete for the agent to use correctly.

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 schema has 0% description coverage for its 10 parameters, but the tool description compensates fully by explaining each parameter: source_type, emr_cluster_id, emr_step_id, s3_project_location, local_log_path, local_project_path, job_entry_point, current_spark_config, provider, and api_key. It clarifies requirements (e.g., 'required if source_type=...') and optionality, making the semantics complete.

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 clearly states the tool's purpose: analyze successful Spark job logs/stats and suggest performance optimizations. It names the specific action (analyze, suggest) and resource (execution log/stats), and differentiates from the sibling tool by emphasizing 'successful' jobs, implying a contrast with failure diagnosis.

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 (for successful jobs to get optimization suggestions) by contrasting with 'successful' and the sibling 'diagnose_spark_failure'. It also explains provider options (including 'none' for raw content) and argument dependencies. However, it does not explicitly state 'use this instead of diagnose_spark_failure when the job succeeded,' leaving the distinction implicit.

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