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finance_forecasting

Destructive

Generate rolling cash and P&L forecasts from ledger data and company drivers, returning month-by-month projections, scenario comparisons, and written analysis of inflection points.

Instructions

Build a rolling cash and P&L forecast from your ledger data and company drivers. Returns a month-by-month projection, scenario comparisons, and a written interpretation of inflection points. Args: message: Free-text objective for the action. horizon_months: Forecast horizon (default 12 months). scenario: Scenario name: base, bull, bear, or a custom label. driver_overrides: Override drivers as JSON dict (e.g. {"revenue_growth": 0.15}).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageNo
scenarioNo
horizon_monthsNo
driver_overridesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior2/5

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

Annotations flag destructiveHint=true and readOnlyHint=false, so this is a mutating tool, yet the description discloses no side effects — nothing about what is written, created, or changed, or whether the forecast is persisted. The 'build' verb and openWorldHint suggest an agentic action, but the description stops at describing outputs and never reconciles why a forecasting tool is destructive.

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?

Two tight sentences cover purpose and outputs, followed by a compact Args block with no fluff. The structure is scannable, though the missing usage and side-effect guidance costs it a 5.

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

Completeness3/5

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

Parameters are documented and an output schema exists, but the definition omits side effects (despite destructiveHint), prerequisites (e.g., a connected ledger source or selected company), and sibling selection guidance. For a tool of this complexity in a sea of forecast siblings, more is needed.

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

Parameters4/5

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

Schema description coverage is 0%, so the description carries the full burden, and it largely delivers: it adds allowed scenario values (base, bull, bear, custom), a concrete driver_overrides example ({"revenue_growth": 0.15}), and clarifies horizon_months' default. The message param ('free-text objective') remains thin, and available driver keys aren't enumerated.

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?

The description opens with a specific verb and resource — 'Build a rolling cash and P&L forecast from your ledger data and company drivers' — and lists concrete outputs (month-by-month projection, scenario comparisons, written interpretation). It is clear on its own, though it does not explicitly name any sibling to differentiate from among the many similar finance_forecast* tools.

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?

No guidance on when to choose this tool over siblings such as finance_forecast_sensitivity, finance_forecast_interpretation, or finance_automl_forecast, all of which overlap in the forecast domain. The only hinted context is 'ledger data and company drivers,' with no exclusions, prerequisites, or alternative routing.

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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