mlflow_runs_get

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

Annotations already indicate readOnlyHint=true, so the description's mention of 'Get' is consistent. It does not add behavioral traits beyond what annotations provide, but it does confirm a read operation. There is no contradiction. A score of 3 is appropriate given annotations cover the safety profile.

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 very short (one sentence plus API endpoint). It is efficient and front-loaded but could be slightly more descriptive without becoming verbose. No wasted words.

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?

Despite having an output schema and annotations, the description fails to contextualize this tool among the many MLflow run siblings (e.g., mlflow_runs_create, mlflow_runs_search, mlflow_runs_list_artifacts). It does not explain that it retrieves a single run by ID, which is implicit but not explicit. For a tool in a large ecosystem, this is insufficient.

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 descriptions for both 'run_id' and 'run_uuid'. The description adds no additional parameter meaning. Baseline is 3 for high coverage.

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 a run' and provides the API endpoint, indicating the action (get) and resource (run). However, it does not specify what attributes of the run are returned (e.g., metadata, metrics). It distinguishes from sibling tools like mlflow_runs_create or mlflow_runs_delete, but is slightly vague about the exact output.

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 like mlflow_runs_search or mlflow_runs_get_output. It lacks any context about prerequisites, when not to use it, or which sibling to prefer for different tasks.

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