MCP Server for OpenMetadata

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

No annotations are provided, so the description carries full burden. It states this is a read operation ('Get'), but doesn't disclose behavioral traits such as authentication requirements, rate limits, error handling, or what 'details' include (e.g., metadata, performance metrics). For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.

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, efficient sentence with no wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word contributes directly to the tool's purpose.

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 no annotations, 0% schema coverage, no output schema, and a read operation with two parameters, the description is incomplete. It lacks details on return values, error cases, and parameter usage, leaving the agent with insufficient context to use the tool effectively beyond basic intent.

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%, so the description must compensate. It mentions 'by ID', which hints at the 'model_id' parameter, but doesn't explain the 'fields' parameter at all. With two parameters and no schema descriptions, the description adds minimal semantic value, failing to clarify parameter purposes or usage.

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 the verb ('Get') and resource ('details of a specific ML model'), making the purpose unambiguous. It distinguishes from sibling 'get_ml_model_by_name' by specifying retrieval by ID rather than name, though it doesn't explicitly mention this distinction. The description avoids tautology by not merely restating the tool name.

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 'get_ml_model_by_name' or 'list_ml_models'. The description implies usage when you have a model ID, but it lacks explicit context, prerequisites, or exclusions, leaving the agent to infer appropriate scenarios.

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