get_databricks_job_costs

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 discloses that the tool returns cost and DBU breakdown per run, with duration, DBU consumed, and estimated cost. It also mentions default date ranges and top_n, which is helpful. However, it does not disclose whether the operation is read-only, if it has side effects, or any auth requirements. Behavioral transparency is adequate but not comprehensive.

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 roughly 50 words, with a clear first sentence stating the purpose. It uses a structured list for parameters. Every sentence adds value—no filler. It is concise and well-organized.

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?

Given the tool has 3 optional parameters, no output schema, and no annotations, the description fairly covers the input and output intent. It explains what is returned (most expensive job runs, cost, DBU, duration) and the default behavior. However, it does not specify output format, sorting order, or pagination, which could be useful. Still, it is nearly complete for this level of complexity.

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 3 parameters with no descriptions (0% coverage). The description adds significant meaning by specifying ISO date format, default values (30 days ago for start_date, today for end_date, and 20 for top_n), and their purpose. This goes well beyond the schema's bare bones, making the parameters clear and usable.

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: 'Show cost and DBU breakdown by Databricks job run.' It specifies the verb 'show' and the resource (cost and DBU breakdown per job run). It distinguishes itself from siblings like 'get_databricks_costs' by focusing on job runs and providing details like duration and DBU. The purpose is specific and actionable.

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 mentions it is 'useful for finding jobs that can be optimised (right-sized clusters, fewer retries, etc.)', which implies usage for optimization. However, it does not explicitly state when to use this tool vs. alternatives such as 'get_databricks_costs' or 'get_databricks_dbu_breakdown', nor does it provide when-not-to-use conditions. Guidance is present but limited.

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