osint

Query Power Generation

query_power_generation_v1

Read-onlyIdempotent

Query verified U.S. monthly net electricity generation (MWh) from EIA-923.

Use this for "how much was generated" questions by state, source-reported balancing authority code, fuel, prime mover, sector, plant, or generator, for a given month. For a fuel total or a fuel mix (e.g. "coal generation", "top fuels"), filter or group by fuel_group — it sums the several energy_source_code values a fuel spans (coal alone is 6 codes), so a total is correct-by-construction; use the raw energy_source_code only when you want one exact as-reported code, since it splits coal/biomass across sub-codes. Select one atom: by_fuel (default — the complete plant total) or by_generator (generator-level, joinable to EIA-860M); never sum across atoms. History runs monthly from 2014-01 onward and is served by default: a bare data_month anywhere in that window answers from the newest promoted vintage covering it, and the response as_of is that knowledge cut (pin as_of to any date to reproduce what was served then — it resolves to the newest vintage at or before it; an empty result names the served window in an empty_scope note). balancing_authority_code is reported by EIA only from 2018 onward — a BA-filtered query cannot see earlier months. Pass filters inside the params object. Returns JSON aggregates with citations down to the exact source month-cell. Does not determine installed capacity (MW — use power.capacity), demand/load, wholesale prices, fuel cost, heat rate, capacity factor, or real-time/hourly dispatch.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`params`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, but the description adds valuable behavioral context: vintage handling, as_of semantics, empty_scope notes, BA data availability from 2018 onward, and citation details. No contradictions with annotations.

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 long but front-loaded with the core purpose and progressively adds essential detail. Some text could be tightened (e.g., repeating 'as_of' mechanics), but every sentence serves a functional purpose given the complex domain and opaque schema.

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

Completeness5/5

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

Despite the complex data domain and minimal schema, the description covers query scope, filtering dimensions, aggregation semantics, historical behavior, BA limitation, output format (JSON with citations), and exclusions. It is fully self-sufficient for an agent to select and invoke the tool correctly.

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

Parameters5/5

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

The input schema is opaque (only a generic params object with 0% property coverage), so the description carries the full burden. It explains the meaning and proper use of key parameters (fuel_group, energy_source_code, atom, data_month, as_of, balancing_authority_code) and warns about subtle aggregation pitfalls (coal split across codes, never sum across atoms), fully compensating for the schema gap.

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 opening sentence clearly states the tool queries verified U.S. monthly net electricity generation (MWh) from EIA-923, with a specific verb and resource. It further distinguishes itself from siblings by listing non-goals (capacity, demand, prices, etc.), making its purpose unambiguous.

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

Usage Guidelines5/5

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

Explicitly states when to use the tool ('Use this for "how much was generated" questions...') and gives concrete selection guidance for fuel_group vs energy_source_code, atom selection, as_of pinning, and BA limitations. It also names alternatives (e.g., 'use power.capacity') for non-generation topics, satisfying the when/when-not criterion.

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

A4/5.0

Disambiguation4/5

Each capability has a clearly named describe/query pair, and domains like power, AI infrastructure, robotics, and space are distinct. The generic describe_capability_v1/query_capability_v1 could be confused with the named variants, and the many ISO-specific interconnection queues share similar names, but descriptions explicitly disambiguate them.

Naming Consistency5/5

All tools follow a consistent lowercase snake_case verb_noun pattern: describe_<capability>_v1 and query_<capability>_v1, with a few utility tools like list_capabilities_v1 and get_source_evidence_v1. There is no mixing of conventions.

Tool Count2/5

At 62 tools, the set is far too large for typical server scope. The describe/query pairs inflate the count even though each is justified, and the generic capabilities plus per-ISO variants make it feel heavy and hard to navigate.

Completeness4/5

The server covers each domain thoroughly with describe, query, and evidence verification, plus generic fallbacks for capabilities not yet in the client's tool list. Minor gaps exist (e.g., no county/state attribution for some data, no load types in ISO queues) but they are explicitly documented and workable.

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