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start_run

Start a new run within an experiment to track data science experiments, log metrics, and manage model training sessions.

Instructions

Start a new run within an experiment

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
experiment_nameYesName of the experiment to run in
run_nameNoOptional name for the run
descriptionNoOptional description of the run
tagsNoOptional tags as key-value pairs
Behavior2/5

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

With no annotations, the description carries the full burden. It does not disclose if starting a run is a state change, if it creates a resource, any side effects, or required permissions. The description is too brief to convey behavioral traits beyond the obvious.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence with no wasted words. It effectively communicates the primary action. However, it could be slightly more structured to include additional context without losing conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's role in experiment lifecycle, the description is too minimal. It lacks details about what happens after starting, how to log data, or the relationship with 'end_run'. The presence of 4 parameters and no output schema or annotations makes this description insufficient for full context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage for all 4 parameters, so the description adds no additional meaning. Baseline 3 is appropriate as the schema already explains the parameters adequately, though the description reinforces the role of 'experiment_name'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Start') and resource ('a new run') with a clear context ('within an experiment'). It distinguishes the tool from siblings like 'end_run' and 'create_experiment' by focusing on the run initiation action.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool vs alternatives, no prerequisites (e.g., experiment must exist), and no exclusions or context about its typical use case. The description lacks any 'when to use' or 'when not to use' information.

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

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