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LiveDataLink

by io.github.blackboxfoundry

eia_electricity_state

Read-onlyIdempotent

Monthly state-level electricity data from EIA. Filter by state (two-letter code or 'US' for national), sector (residential / commercial / industrial / transportation / all), and metric (price / sales / revenue / customers / generation). Default: US, all sectors, price.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNoInclusive upper-bound period (ISO date or YYYY-MM).
limitNoMaximum rows to return (default 50, max 5000).
startNoInclusive lower-bound period (ISO date or YYYY-MM depending on series cadence).
stateNoTwo-letter state code (e.g. 'TX', 'CA') or 'US' for national rollup. Default 'US'.
metricNoMetric: 'price', 'sales', 'revenue', 'customers', 'generation'. Default 'price'.
sectorNoSector: 'all', 'residential', 'commercial', 'industrial', 'transportation'. Default 'all'.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already cover the read-only, idempotent, non-destructive safety profile, so the description does not need to repeat that. It adds useful behavioral context such as monthly cadence, the three filtering axes, and the 'US / all sectors / price' default, but it does not describe output shape or date-range behavior beyond what the schema already provides.

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 two dense sentences with no filler. It front-loads the resource, immediately lists the three filtering dimensions, and closes with defaults, making it easy to scan and parse.

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 high schema coverage and read-only annotations, the description is sufficient for selecting the tool and making an initial call with the stated defaults. It does not describe the output format, and since there is no output schema, a little more detail about returned fields would improve completeness, though the metric names already hint at the result structure.

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 description coverage is 100%, so the baseline of 3 applies. The description mostly restates the schema's allowed values and defaults (state codes, sectors, metrics) without adding new meaning for start, end, or limit.

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 identifies the resource ('monthly state-level electricity data from EIA') and enumerates the filtering dimensions (state, sector, metric), with defaults. The stated scope distinguishes it from sibling EIA tools focused on gasoline, natural gas, oil, and renewables.

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 intended use is implied by the state-level electricity scope and the filter dimensions, but the description does not explicitly name alternative tools or provide when-to-use/when-not-to-use guidance. It leaves routing decisions to inference rather than stating exclusions.

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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TDQS

B3.3/5.0
Disambiguation2/5

Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.

Naming Consistency3/5

Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.

Tool Count1/5

294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.

Completeness4/5

The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.

Resources