Retrieve detailed information about a logged MLflow model by its model ID, including metadata and properties.
Get detailed information about a specific logged model by its ID.
Args: model_id: The logged model ID (obtained from search_logged_models results).
| Name | Required | Description | Default |
|---|---|---|---|
| model_id | Yes |
Changes observed during successful MCP inspections.
v0.4.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the description doesn't need to restate read-only. It adds the source of the model ID but no additional behavioral traits (e.g., no mention of permission or side effects).
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?
Extremely concise: two sentences that front-load the main purpose and include a bullet for the parameter. No extraneous 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?
With no output schema, the description only says 'detailed information', which is vague. It does not describe the returned fields or structure. While the tool is simple, an agent may benefit from knowing what properties are returned.
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 0%, but the description adds a meaningful hint about the parameter source ('obtained from search_logged_models results'), partially compensating for the lack of schema-level description. However, it doesn't specify format or constraints.
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?
Description clearly states the verb 'Get' and the resource 'logged model', specifying the unique identifier (model ID) and its source from search_logged_models results. This distinguishes it from siblings like get_model_version or get_registered_model.
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 clear context by stating that the model_id is obtained from search_logged_models results, giving a prerequisite. However, it does not explicitly exclude alternatives or state when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.