trigger_run

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

No annotations are provided, so the description carries full burden. It states the action ('Triggers a run') and return value, but lacks behavioral details such as permissions required, whether the run is synchronous/asynchronous, error handling, rate limits, or side effects. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

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-structured and front-loaded with the core purpose, followed by clear sections for Args and Returns. Every sentence earns its place by defining parameters and output without redundancy. It's appropriately sized for a tool with two parameters and an output schema.

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 a mutation tool with no annotations, 0% schema coverage, but an output schema (which handles return values), the description is partially complete. It covers purpose and parameters adequately but lacks behavioral context (e.g., execution model, errors). The output schema reduces the burden, but more guidance on usage and transparency would improve completeness for this complexity level.

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. It adds meaning by explaining 'job_id' as 'The ID of the job to run' and 'job_args' as 'A list of Python parameters to pass to the run', which clarifies their roles beyond schema types. However, it doesn't detail format constraints (e.g., job_id source, job_args syntax), leaving some ambiguity. Baseline 3 is appropriate as it adds value but not fully comprehensive.

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 with a specific verb ('Triggers') and resource ('run of the specified job'). It distinguishes from siblings like 'create_job' (creation vs. execution) and 'list_job_runs' (listing vs. triggering), though it doesn't explicitly mention these distinctions. The purpose is unambiguous but could be more explicit about sibling differentiation.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., job must exist), exclusions, or comparisons to siblings like 'list_job_runs' for monitoring runs. Usage is implied by the purpose but lacks explicit context or decision criteria.

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