Calibrate up to 25 predictions in a single MCP call (flat $0.005 per call, regardless of batch size). Each item must include prediction; optional confidence, domain, stakes. Returns an array of calibration results matching the input order.
| Name | Required | Description | Default |
|---|---|---|---|
| predictions | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description covers pricing, batch size limit, required/optional fields, and result ordering. It lacks details on error handling, idempotency, or latency, leaving gaps in behavioral disclosure.
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?
Two concise sentences deliver all key information: batch limit, pricing, field requirements, and result order. No extraneous content.
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 output schema or annotations, the description provides input format, constraints, and pricing. It misses error scenarios and validation rules, but is largely sufficient for basic usage.
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 description identifies required vs. optional fields (prediction, confidence, domain, stakes) but adds no additional meaning beyond what the schema already provides. Schema coverage is 0%, and the description does not compensate with explanations of field semantics.
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 'calibrate' and the resource 'predictions' in a batch context, limiting to 25 items. It distinguishes from sibling 'calibrate_decision' by emphasizing batch processing.
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 implies usage for multiple predictions with batch pricing and field requirements. It does not explicitly state when not to use or name alternatives, but sibling 'calibrate_decision' serves as the single-prediction counterpart, providing implicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.