osint

Query FERC Form 1 Plant Costs

query_power_plant_costs_v1

Read-onlyIdempotent

Query accepted native-XBRL FERC Form 1 large-steam plant facts.

Returns one separately cited XBRL fact per respondent, report year, raw plant name, and source concept: installed capacity, net generation, plant-cost balance items, fuel expense, and individual operation and maintenance lines. Values and units are as filed; nominal dollars are not adjusted, allocated, divided by generation, or combined into a derived O&M total. Plant names remain raw strings with no EIA ID matching. Every value verifies against its exact archive member, XBRL fact ID, context, concept, and SHA-256.

Coverage is the Form 1 filing population, not the full fleet. ERCOT-only merchant entities largely do not file, so ERCOT coverage is partial and plant absence means not present in served filings—not zero. ercot_relevance=true is a one-call filter based only on the respondent's raw states-served disclosure mentioning Texas; it is not a plant-location claim. v0 covers accepted native XBRL (report year 2021 onward) and the large-steam schedule including nuclear; hydro, pumped storage, migrated historical XBRL, and Visual FoxPro-era filings are explicitly outside v0.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`params`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4/5.0

Behavior4/5

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

Annotations already indicate idempotent, read-only, non-destructive behavior. The description adds substantial behavioral context: format of returned facts, no adjustments or EIA matching, verification details, and coverage nuances. This goes beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a dense single paragraph. While it packs much useful information, it lacks structure (e.g., bullet points) and could be more concise. Some sentences are long and complex.

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

Completeness3/5

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

The description covers data scope, limitations, and behavioral details well, but fails to describe the input parameter schema or provide examples. Given the output schema exists, return values are likely covered, but the missing parameter guidance is a significant gap.

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

Parameters2/5

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

The input schema has a single 'params' object with no defined properties (0% schema coverage). The description does not explain how to construct or use the 'params' argument, leaving a critical gap for the AI agent.

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 states it queries accepted native-XBRL FERC Form 1 large-steam plant facts, listing specific data types. It distinguishes itself from siblings like describe_power_plant_costs_v1 by focusing on raw data retrieval rather than description.

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?

The description explicitly states what it returns and does not return, including coverage limitations, partial ERCOT coverage, and exclusions (hydro, migrated historical XBRL, etc.). It provides clear guidance on when to use and when not to use, and implies alternatives.

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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