forecast-mcp

explain_forecast

Get a plain-language explanation of why a specific SKU was assigned to its forecast model tier, making the routing logic clear for non-technical users.

Instructions

Return a plain-language explanation of why a SKU was routed to the model tier it was, for surfacing to a non-technical user.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "unique_id": "<unique_id>"
    },
    "name": "explain_forecast"
  }
}

Input Schema

Name	Required	Description	Default
`unique_id`	Yes	SKU identifier.

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 18, 2026

TDQS

A3.6/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of disclosing behavior. It only describes the output (plain-language explanation) but does not mention that the tool is read-only, what happens if the SKU does not exist, or any side effects. For a read-operation with no destructive potential, a statement about being safe to call would be valuable.

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

Conciseness5/5

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

The description is a single sentence that is front-loaded, concise, and free of unnecessary words. Every element contributes to understanding the tool's purpose.

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

Completeness4/5

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

Given the tool has one parameter, an output schema (not shown but exists), and no annotations, the description is largely complete. It specifies the output type (plain-language explanation) and audience (non-technical user). However, it could be slightly more complete by mentioning error handling (e.g., invalid SKU) or that it is a read-only operation. For a simple tool, this is adequate.

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

Parameters3/5

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

Schema coverage is 100% – the parameter `unique_id` is described as 'SKU identifier.' The tool description adds context by stating the tool explains why a SKU was routed to a model tier, but it does not add parameter-specific details like format examples or constraints. The description reaffirms the tool's purpose rather than the parameter's semantics, meeting the baseline for high coverage.

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

Purpose5/5

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

The description clearly states the action: 'Return a plain-language explanation of why a SKU was routed to the model tier it was, for surfacing to a non-technical user.' It specifies the verb ('return'), the resource (SKU routing decision), and the output format (plain-language). This distinguishes it from siblings like list_skus (listing SKUs) and classify_demand_pattern (classifying patterns).

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

Usage Guidelines3/5

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

The description implies when to use the tool (when a non-technical explanation of tier assignment is needed), but it does not explicitly state when to use it vs. alternatives, nor does it provide exclusions or prerequisites. For example, it could clarify that this is not for evaluating forecast accuracy (evaluate_forecast) or generating forecasts (forecast_series).

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

Deploy Server