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azmartone67

DC Hub — Data Center & Energy Intelligence

Get Retirement Headroom

get_retirement_headroom
Read-onlyIdempotent

Identify power plant retirements within a horizon and region to locate freed grid capacity, filtering by minimum MW and fuel type, returning nearby substations and queue details.

Instructions

Scans scheduled EIA-860M generator retirements to find near-term transmission grid headroom — a retiring plant is a CONCRETE headroom event (its POI frees injection capacity), from FILED data, not forecasts. Returns _entity=retirement_headroom_results: retiring generators inside your horizon (name, MW, fuel, prime mover, retirement_date), representative_point, nearest substations with distance_km + count within 25 km, county-level queue_pressure (competing in-progress MW), iso_context (the generator's own EIA balancing-authority code), and a pre-filled site_evaluation_handoff (analyze_site + get_water_risk args, capacity_mw = YOUR target load). Answers "where is grid capacity about to free up", "which retiring plants open injection headroom near me". Try: get_retirement_headroom target_mw=50 horizon_months=18 region_iso=MISO — "50 MW opening near a substation inside 18 months, sidestepping the 4-7yr mega-queue." Honesty: meta.caveat flags that filed dates are subject to ISO reliability reviews (RMR extensions). Use to find WHERE capacity opens next; for what's already queued use get_refined_queue; for one site use analyze_site.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (1-500; default varies by tool)
target_mwYesMinimum required headroom in megawatts (MW) — filters to retiring generators at/above this size. Also passed through as the handoff's analyze_site capacity_mw (the DC you are siting).
region_isoNoOptional target region or ISO (e.g., 'MISO', 'PJM', 'ERCOT', 'SPP', 'CAISO', 'NYISO', 'ISONE'). Matches the generator's own EIA balancing-authority code — real market boundaries, not state lines. Comma-separated for a union.
fuel_filterNoOptional filter for retiring fuel categories, substring-matched (e.g., 'Coal', 'Natural Gas', 'Petroleum').
horizon_monthsYesTime horizon in months to look ahead for planned retirements, 1-120 (e.g., 12, 18, 36).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
quotaNoCaller quota state (remaining calls, tier) when available.
_entityNoPayload class discriminator (e.g. facility|market|iso_grid|queue_results|deal|report|response) — branch on this before parsing the rest.
citationNoMachine-readable citation: how to attribute DC Hub (dchub.cloud) for this payload. Normally an OBJECT {source, url, license, cite_as, retrieved_at}; a bare string is accepted and carries the attribution line itself.
provenanceNoCollection-level provenance block: {source, method, as_of, verification_counts, cite_url_template, license, cite_as}. Quote the verification level when citing.
_front_doorNoIn-band front-door hint (first workflow-entry tool of a session): call plan_query(intent) first for the ordered multi-step plan.
_return_loopNoSuggested next-session delta call (get_changes since=24h) so you pull only what changed.
site_evaluation_handoffNoPre-built follow-up calls (analyze_site / get_water_risk args) when the payload carries coordinates — an array of {tool, parameters, why} entries.
Install Server

TDQS

A5/5.0
Behavior5/5

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

Annotations already signal readOnlyHint, idempotentHint, and destructiveHint:false, and the description adds meaningful behavioral context beyond that: data comes from FILED submissions rather than forecasts, and meta.caveat flags that filed dates are subject to ISO reliability reviews and RMR extensions. This gives the agent an accurate model of data reliability without contradicting annotations.

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 long but dense and well-ordered: main purpose first, output entities next, example call, honesty caveat, then sibling routing. Every sentence contributes operational guidance, with no filler or repetition of structured fields.

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?

The description covers purpose, output shape, parameter semantics, a worked example, data caveats, and explicit alternatives. Combined with the output schema and annotations, an agent has everything needed 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?

Schema coverage is 100%, but the description adds crucial semantic relationships: target_mw is also passed through as the handoff's analyze_site capacity_mw, region_iso matches the generator's own EIA balancing-authority code rather than state lines, and fuel_filter is substring-matched. These details materially improve correct parameter use beyond the schema alone.

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 states a specific verb and resource: it scans scheduled EIA-860M generator retirements to find near-term transmission grid headroom. It directly answers user questions like 'where is grid capacity about to free up' and distinguishes itself from siblings by naming get_refined_queue and analyze_site as alternatives.

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?

It provides explicit routing guidance: 'Use to find WHERE capacity opens next; for what's already queued use get_refined_queue; for one site use analyze_site.' It also includes a concrete example call with parameters, making when and how to use the tool unmistakable.

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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