get_mlflow_runs

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It conveys that the tool is a read-like operation and names the returned data categories, but it omits any behavior around ordering, time ranges, pagination, performance cost, or authentication requirements. It is not misleading, but it is thin for a data retrieval tool.

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 one compact sentence with no filler. The action, resource, and expected result are front-loaded, making it easy to parse quickly.

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?

The definition is mostly sufficient for a simple filtered read tool: it names the target and the returned fields, and the schema covers all parameters. However, with no annotations, no output schema, and no sibling routing, it leaves the agent without clarity about sorting, default ordering, or when to prefer other run-related tools.

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 100%, so each parameter (limit, filter, experimentId) is already documented structurally. The description adds no new parameter-level meaning beyond the schema, so a baseline score of 3 is appropriate.

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 identifies a specific verb ('Get') and resource ('MLflow runs for an experiment'), with output components named: metrics, parameters, and status. It does not explicitly name sibling tools for differentiation, but the MLflow-scoped phrasing makes the purpose distinct from generic pipeline/training run tools.

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 about when to use this tool versus related alternatives like get_training_runs, get_pipeline_runs, or compare_model_runs. No context is provided about scenarios, exclusions, or prerequisites, so an agent must infer usage from the name and schema alone.

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