mlflow_experiments_get

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

Annotations already declare readOnlyHint=true, indicating a read operation. The description reinforces this by stating it's a GET request. No additional behavioral traits (e.g., required permissions, pagination) are disclosed. The description does not contradict annotations.

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 a single concise sentence that includes the API endpoint. It is front-loaded with the purpose. However, it could be slightly more informative without adding bulk.

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 that there is an output schema, the description does not need to explain return values. The tool is simple (one parameter, read-only). The description adequately covers the core purpose, but could mention that the experiment_id is required or that it returns a single experiment.

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 coverage is 100% with the parameter 'experiment_id' having a description 'Experiment ID'. The description does not add any extra meaning beyond what the schema provides, so baseline 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 states 'Get an experiment by ID', specifying the verb (Get) and the resource (experiment by ID). It includes the API path for reference. Among siblings like mlflow_experiments_create, delete, list, update, this tool is distinctly the get operation.

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?

No guidance is provided on when to use this tool versus alternatives like mlflow_experiments_list or mlflow_experiments_get vs other get operations. The description does not mention prerequisites, context, or when not to use it.

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