model_performanceProduction model metadata plus held-out backtest results: AUC-ROC, AUC-PR and Brier score. (Source: BRR ML model.)
This tool declares no required parameters. Add optional parameters from the input schema as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {},
"name": "model_performance"
}
}| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds useful context about the returned content, including production metadata and held-out backtest metrics, but it does not describe return structure, freshness, or any access constraints.
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 compact sentence with the key content front-loaded, followed by a brief source note. Every part contributes useful information without unnecessary wording.
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?
For a no-parameter, read-only tool with no output schema, the description is reasonably complete: it states the data source and the main metrics returned. It could be stronger by clarifying the return shape or model scope, but the core information an agent needs is present.
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?
The tool has zero parameters, so there are no parameter semantics to explain. The baseline for a no-parameter tool is 4, and the description does not need to compensate for missing schema details.
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 identifies the resource as production ML model metadata and held-out backtest results, and names the specific metrics returned: AUC-ROC, AUC-PR, and Brier score. It does not explicitly distinguish this tool from the sibling mortgage_performance tool, so it falls short of a 5.
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 gives no explicit guidance on when to use this tool versus alternatives, nor does it state any exclusions or prerequisites. An agent can infer it is for model performance, but that is only implied usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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