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Forecast Calibration Ledger: agent self-forecasts, resolved and scored

fcl_resolve

Resolve a forecast against its run record ($0.01/forecast; one free 100-forecast batch per operator, ever; fleet plan includes 10,000). Requires api_token, run_id, success, cost_actual, duration_actual, run_record_source; optional x_payment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
run_idYes
successYes
api_tokenYes
x_paymentNo
cost_actualYes
duration_actualYes
run_record_sourceYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.9/5.0
Behavior3/5

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

With no annotations, the description does disclose high-value behavior an agent must know: a $0.01 per-forecast charge, a one-time free 100-forecast batch, a 10,000 fleet-plan allotment, and that payment can be supplied via x_payment. It omits whether resolution is idempotent, whether it can be re-run, what side effects occur on the forecast record, and what is returned.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single dense sentence with the billing model front-loaded in parentheses and the parameter requirements after; there is no filler. It is terse to the point of terseness but every clause carries information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

A seven-parameter mutation tool with zero schema descriptions, no annotations, and no output schema needs the description to define parameter meanings and effects. Coverage of pricing and required fields is useful, but an agent still cannot determine units, formats, or outcomes from this text alone.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% across seven parameters, so the description must carry semantics, yet it only restates parameter names in a list. Nothing explains what run_record_source should contain, what units cost_actual and duration_actual use, or what setting success=true triggers.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Resolve a forecast against its run record', which tells an agent this is a write-back of run results for an existing forecast. It does not contrast itself with siblings fcl_forecast/fcl_calibration/fcl_weight, so the boundary between resolving and forecasting is left implicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description lists prerequisites (required api_token, run_id, etc.) and the billing model, but never says when to call this versus fcl_forecast or fcl_calibration, nor what happens if the run record is missing. No exclusions or alternative routing are given.

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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