Record experiment outcomes by attaching conclusions and decisions to GPU job metrics, separating scientific interpretation from immutable infrastructure artifacts.
Attach scientific interpretation separately from immutable infrastructure artifacts.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | ||
| decision | Yes | ||
| conclusion | Yes | ||
| new_metric | Yes | ||
| baseline_metric | Yes |
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 must carry the full burden of behavioral disclosure. It implies a write operation ('Attach') but does not state whether this creates, updates, or mutates anything, what side effects occur, whether it is destructive, or what the return behavior is. The phrase 'immutable infrastructure artifacts' hints at separation but does not explain actual tool 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 short, but brevity is not conciseness when it delivers almost no information. The single sentence is front-loaded but vague, and every word fails to add operational value. It is under-specified rather than efficiently specified.
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?
This tool has 5 required parameters, no output schema, no annotations, and no parameter documentation, yet the description explains none of the inputs, outcomes, or constraints. An agent has essentially no basis for correct invocation. This is completely inadequate for the tool's 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?
Schema description coverage is 0%, and the description adds no meaning about any of the five parameters. Terms like 'scientific interpretation' might loosely map to conclusion/decision, but the description never explains job_id, baseline_metric, new_metric, decision, or conclusion. With no parameter documentation, an agent cannot reliably populate the required inputs.
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 'Attach scientific interpretation separately from immutable infrastructure artifacts' is metaphorical and vague. It does not clearly state that this tool records an experiment result, nor does it mention the metrics, decision, or conclusion that the schema shows are central. It also does not distinguish itself from siblings like list_ready_results or submit_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?
There is no guidance on when to use this tool versus alternatives such as submit_job or list_ready_results. No context, prerequisites, or exclusions are provided. The only hint is that it relates to scientific interpretation, but this is not actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.