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REE MCP Server

REE MCP Server

by ESJavadex
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_peak_analysis

Identify peak demand patterns and calculate load factors by analyzing daily maximum and minimum electricity demand over a user-defined date range.

Instructions

Get peak demand analysis over a period.

Analyzes daily maximum and minimum demand to identify patterns and calculate load factors.

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

Returns: JSON string with peak demand analysis.

Examples: Get peak analysis for a week: >>> await get_peak_analysis("2025-10-01", "2025-10-07")

Get peak analysis for a month:
>>> await get_peak_analysis("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": "get_peak_analysis"
  }
}

Input Schema

NameRequiredDescriptionDefault
start_dateYes
end_dateYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.2/5.0
Behavior2/5

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

No annotations provided, so the description must carry the full burden. It states the tool 'analyzes' and 'calculates,' implying a read-only operation, but does not explicitly confirm non-destructive behavior, rate limits, or any side effects. The lack of explicit transparency about safety or performance is a gap.

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?

The description is well-structured with an overview, args, returns, and examples. It is front-loaded with the purpose. However, the examples could be shortened; overall it is concise enough for the complexity.

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's simplicity (two parameters, no annotations, output schema exists), the description covers the core purpose, required args, and return format with examples. The missing annotation information reduces completeness slightly, but the provided elements are adequate.

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 coverage is 0%, but the description adds format constraints (YYYY-MM-DD) and provides examples for usage. This adds meaning beyond the bare schema, though no further details on allowed values or behavior are given.

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 clearly states the tool retrieves peak demand analysis over a period, analyzing daily maximum and minimum demand to identify patterns and calculate load factors. While it is specific, it does not explicitly distinguish it from siblings like get_daily_demand_statistics, which may overlap.

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 use this tool versus alternatives. The description does not mention prerequisites, exclusions, or sibling tools, leaving the agent to infer usage context.

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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