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DC Hub — Data Center & Power Intelligence

by cloud.dchub

Get Retirement Headroom

get_retirement_headroom
Read-onlyIdempotent

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.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, lowering the bar. The description adds valuable behavioral context: it operates on FILED data not forecasts, and honestly discloses that meta.caveat flags RMR-related reliability review risks. It also explains the handoff payload behavior. No contradiction 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 dense but every sentence earns its place: core purpose, output fields, use-case answers, worked example, caveat, and sibling routing. It is front-loaded with the most actionable material, though it could be mildly condensed without losing critical information.

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?

For a sophisticated data-scanning tool with an output schema, this description is remarkably complete. It names the data source (EIA-860M), explains the headroom event, lists the return fields (including _entity, representative_point, substations, queue_pressure, iso_context, handoff), and discloses a key caveat. The agent has everything needed to decide and invoke correctly.

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 coverage is 100%, so the schema already fully documents all five parameters, which is the baseline 3. The description only restates that target_mw is passed through as capacity_mw in the handoff, which the schema already states. The worked example reinforces usage but adds no new semantic detail about the parameters.

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 opens with a specific verb and object ('Scans scheduled EIA-860M generator retirements to find near-term transmission grid headroom') and immediately clarifies the core concept (a retiring plant frees injection capacity). It also explicitly differentiates from siblings by naming get_refined_queue and analyze_site, so the agent can select this tool without opening other schemas.

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 gives explicit when-to-use guidance with alternatives: 'Use to find WHERE capacity opens next; for what's already queued use get_refined_queue; for one site use analyze_site.' It also answers natural-language question forms and provides a concrete parameterized example (target_mw=50 horizon_months=18 region_iso=MISO), making the usage conditions 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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TDQS

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes despite some thematic overlap, and each description includes explicit 'Do NOT use' guidance to prevent misselection. However, a few pairs like search_intelligence vs semantic_search are nearly identical in function, and the sheer number of tools increases the chance of selecting the wrong one without careful reading.

Naming Consistency4/5

The vast majority of tools follow a predictable 'get_*' prefix for data reads, and many others use verb_noun patterns (analyze_*, rank_*, save_*, set_*). There are a handful of outliers like ai_capacity_index, grid_transition_radar, and site_selection_canvas that break the pattern, but overall the conventions are consistent enough for an agent to infer meaning.

Tool Count2/5

With 82 tools, this server is extremely heavy compared to typical MCP servers (3-15 tools). While the domain is broad, many tools serve narrow sub-purposes and could be consolidated (e.g., multiple site-scoring variants, multiple grid telemetry endpoints). The count overwhelms an agent's ability to choose efficiently and feels like over-fragmentation rather than necessary granularity.

Completeness4/5

The tool surface covers the full lifecycle of data-center siting intelligence: site analysis, grid, fiber, water, climate, tax, permitting, deals, news, saved-site management, and meta-planning. Minor gaps exist (e.g., no delete or update operations for saved sites), but the core workflows are well-supported and the descriptions are comprehensive.

Resources