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visethchapman

energy-mcp

by visethchapman
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
ENERGY_MCP_DBNoPath to an alternative SQLite database file. If not set, the server uses the bundled data file.

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
list_regionsA

List the balancing authorities available, with row counts and the date range of demand data for each. Call this first to see what you can query.

query_demandA

Return hourly electricity demand (MWh) for one region over a date range.

Args:
    region: Balancing authority code (e.g. ERCO, CISO, PJM, NYIS).
    start: Inclusive start date, 'YYYY-MM-DD'.
    end: Inclusive end date, 'YYYY-MM-DD'.
    limit: Max rows to return (default 168 = one week of hours).

Returns hourly rows ordered by time. For long ranges prefer daily_demand.
daily_demandA

Return daily demand statistics (average, peak, minimum MWh) for one region over a date range. Use this instead of query_demand for ranges longer than a few days.

Args:
    region: Balancing authority code.
    start: Inclusive start date, 'YYYY-MM-DD'.
    end: Inclusive end date, 'YYYY-MM-DD'.
peak_demandA

Find the single highest-demand hour for one region over a date range, returning the timestamp and the demand in MWh.

Args:
    region: Balancing authority code.
    start: Inclusive start date, 'YYYY-MM-DD'.
    end: Inclusive end date, 'YYYY-MM-DD'.
compare_regionsA

Compare average and peak demand across regions over the same date range. Useful for questions like "which grid was busiest last summer?".

Args:
    start: Inclusive start date, 'YYYY-MM-DD'.
    end: Inclusive end date, 'YYYY-MM-DD'.
    regions: Optional subset of region codes. Defaults to all regions.
demand_vs_weatherA

Join daily average demand against daily temperature for a region, using the NOAA weather station nearest that region's main load zone. Reveals the demand-temperature relationship (heating and cooling load).

Args:
    region: Balancing authority code.
    start: Inclusive start date, 'YYYY-MM-DD'.
    end: Inclusive end date, 'YYYY-MM-DD'.

Returns one row per day: average demand (MWh) and that day's max/min temp (C).

Prompts

Interactive templates invoked by user choice

NameDescription
analyze_demandA starter prompt for analyzing a region's demand using these tools.

Resources

Contextual data attached and managed by the client

NameDescription
schema_resourceHuman-readable description of the tables behind these tools.
coverage_resourceWhat data is actually loaded right now (regions, row counts, date range).
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.4/5.0

Scored across 6 tools

Disambiguation5/5

Each tool serves a distinct purpose: listing regions, retrieving hourly/daily/peak demand, comparing across regions, and correlating demand with weather. No functional overlap.

Naming Consistency5/5

All six tool names follow a consistent verb_noun pattern with snake_case: list_regions, query_demand, daily_demand, peak_demand, compare_regions, demand_vs_weather. Naming is predictable and clear.

Tool Count5/5

With 6 tools, the server is well-scoped for its purpose – covering region discovery, multiple demand granularities, inter-region comparison, and weather integration. No bloat or deficiency.

Completeness4/5

Core demand querying workflows are fully supported. A minor gap is the lack of a raw hourly multi-region query (compare_regions only returns averages/peaks). Overall, the surface is thorough for typical use cases.

Maintenance

ActivitySlowing
ResponsivenessNo issues

Resources