osint

Query Power Fuel Receipts and Costs

query_power_fuel_cost_v1

Read-onlyIdempotent

Query verified raw EIA-923 fuel receipts and delivered fuel costs.

Returns one Page 5 Fuel Receipts and Costs row per published receipt: plant/month, fuel, supplier, purchase type, source physical quantity, and delivered cost in EIA's stated cents/MMBtu. Filter by plant, month/range, exact source strings, state, fuel, cost status, or source-reported balancing authority code; {"state":"TX","balancing_authority_code":"ERCO"} returns an ERCOT slice in one call. Quantity units remain fuel-specific (short tons, barrels, or Mcf).

EIA withholds costs for some plants. The raw . marker is preserved in fuel_cost_raw, the numeric cost is null, and fuel_cost_status explicitly reports withheld for unregulated receipts. Missing is never zero or imputed. This tool does not derive heat rates, efficiency, marginal cost, generation cost, or $/MWh; combine the cited raw atoms outside exascale.build if analysis requires those judgments. Every quantity or cost can be verified against its exact workbook cell.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`params`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A5/5.0

Behavior5/5

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

The description reveals important behavioral details beyond the readOnlyHint annotation: EIA withholds costs for some plants, the raw '.' marker is preserved in fuel_cost_raw, numeric cost is null, and fuel_cost_status reports 'withheld'. It explicitly states missing values are never zero or imputed, and that every quantity can be verified against its workbook cell.

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 information-dense yet well-structured: it opens with a clear purpose, then details filtering and data handling, closes with limitations. Every sentence adds value without redundancy. It is appropriately sized for the tool's complexity.

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?

Given the tool's complexity and the presence of an output schema, the description covers purpose, filtering options, data nuances, limitations, and verification. It leaves no significant gaps for an agent to misinterpret the tool's behavior or output.

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?

Despite 0% schema description coverage, the description compensates thoroughly by listing all major filterable fields (plant, month/range, exact source strings, state, fuel, cost status, balancing authority code) and providing a working example. This gives the agent full parameter understanding.

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 the tool queries verified raw EIA-923 fuel receipts and costs, specifying it returns Page 5 Fuel Receipts and Costs rows with detailed fields. The verb 'Query' combined with the resource 'power_fuel_cost' and the contrast with sibling 'describe_power_fuel_cost_v1' make the purpose precise and distinguishable.

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 the tool does not do (derive heat rates, efficiency, marginal cost, etc.) and advises combining raw atoms elsewhere for such analysis. It also provides a concrete filter example `{"state":"TX","balancing_authority_code":"ERCO"}`, giving clear guidance on usage context and 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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