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REE MCP Server

REE MCP Server

by ESJavadex
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

analyze_demand_volatility

Calculate daily demand swings and load factors to identify high-variability days and overall stability patterns over a specified date range.

Instructions

Analyze demand volatility patterns over a period.

Calculates daily demand swings, load factors, and volatility levels to identify high-variability days and overall stability patterns.

Args: start_date: Start date in YYYY-MM-DD format end_date: End date in YYYY-MM-DD format

Returns: JSON string with volatility analysis and stability assessment.

Examples: Analyze volatility for a week: >>> await analyze_demand_volatility("2025-10-01", "2025-10-07")

Analyze volatility for a month:
>>> await analyze_demand_volatility("2025-10-01", "2025-10-31")

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": {
      "start_date": "<start_date>",
      "end_date": "<end_date>"
    },
    "name": "analyze_demand_volatility"
  }
}

Input Schema

NameRequiredDescriptionDefault
start_dateYes
end_dateYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.0.0

TDQS

A3.8/5.0
Behavior3/5

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

No annotations provided, so the description carries full burden. It mentions returning a JSON with volatility analysis and stability assessment, but does not disclose computational cost, data freshness, or side effects (though likely read-only). Acceptable but not thorough.

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?

Description is concise: a clear purpose line, structured args/returns sections, and examples. No fluff, front-loaded with key information.

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?

Output schema exists, so description needn't detail return structure. It provides high-level outcome and example usage. Given 2 required parameters and clear examples, it is fairly complete.

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?

Input schema has 0% description coverage, but the description adds YYYY-MM-DD format and examples showing start/end dates. This compensates for the schema's lack of parameter details.

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 explicitly states it analyzes demand volatility over a period, calculating daily demand swings, load factors, and volatility levels. This clearly distinguishes it from siblings that fetch data (e.g., get_daily_demand_statistics) or compare (compare_forecast_actual), providing a specific verb and resource.

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 explicit guidance on when to use this tool versus alternatives like get_daily_demand_statistics or compare_forecast_actual. While examples show date range usage, they do not differentiate use cases or mention prerequisites.

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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