mlflow_experiments_update

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

The annotation readOnlyHint=false already indicates mutation, but the description does not add any behavioral details beyond the endpoint. It fails to disclose side effects, permissions, or what happens to existing settings. For a mutation tool, this is a significant gap.

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 sentence, but it is under-specified. While not verbose, it does not earn its place by providing useful information. It is too minimal to be considered effectively concise.

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 the tool has 4 parameters, is a mutation, and has annotations, the description is insufficient. It does not cover return values, error scenarios, or change scope. Even combined with schema and annotations, the description leaves critical gaps.

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 50%, with two parameters having minimal descriptions. The tool description does not add any meaning to the parameters; it does not explain the format of tags or the effect of lifecycle_stage. Since the description fails to compensate for the incomplete schema, it adds no 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 'Update an experiment', which identifies the verb and resource. However, it does not differentiate from sibling tools like mlflow_experiments_create or mlflow_experiments_delete, nor does it specify which attributes can be updated. It is clear but lacks sibling differentiation.

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 provides no guidance on when to use this tool versus alternatives. There is no mention of context, prerequisites, or when to prefer update over create or other experiment tools. The agent is left without any usage hints.

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