Execute a Hydra ETL pipeline and return the execution log. Use only when the user explicitly requests a run, as it writes real data to the destination.
Run a Hydra ETL job and return the execution log. Only call this tool if the user explicitly asked for the run: it writes real data to the destination.
| Name | Required | Description | Default |
|---|---|---|---|
| job_path | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as destructiveHint=true and readOnlyHint=false, and the description reinforces that it writes real data to the destination. It adds the 'explicit user request' caution, but does not disclose other behavioral traits like execution duration, blocking behavior, or partial failure modes.
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?
Two sentences with no filler. The first sentence states the core action and output; the second delivers an important safety cue. The most critical behavioral warning is front-loaded.
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 one-parameter destructive tool with an output schema and annotations, the description covers the main risk and return behavior. However, the missing semantics of job_path leave an agent uncertain about what value to pass, and there is no pointer to validation or preview alternatives before performing a real data write.
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 for the one parameter, job_path. It does not explain the expected path format, whether it is absolute/relative, or how to obtain valid job paths from sibling tools like hydra_list_jobs. The parameter name is somewhat self-explanatory, but the description adds no real semantic value.
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 action ('Run'), the resource ('a Hydra ETL job'), and the return value ('return the execution log'). This distinguishes it from sibling tools like hydra_read_job, hydra_validate_job, and hydra_write_job.
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 gives an explicit when-not-to-use condition: only call when the user explicitly asked for the run, because it writes real data. It does not name alternatives such as validate_job or preview_data, but the consent requirement is a strong usage signal.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.