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azmartone67

DC HUB

by azmartone67
OverviewSchemaRelated ServersScoreDiscussions(1)
JavaScript
Remote

Market Scenario Simulator

simulate_scenario
Read-onlyIdempotent

Analyze how changes in power price, queue wait, and other factors affect market rankings across 300+ DC Hub markets. Get baseline vs scenario composite with formula breakdown.

Instructions

Counterfactual WHAT-IF re-scoring of 300+ DC Hub power markets under YOUR explicit deltas — answers "what happens to the market ranking if conditions change" (only DC Hub holds the underlying components). Params (all optional, pass at least one delta): avg_kwh_cents_pct (power-price % change, e.g. 30), time_to_power_months_delta (months added/removed), queue_wait_months_delta, reserve_margin_pct_delta (points), curtailment_pct_delta (points), market (one slug, e.g. abilene), top_n (default 10, max 25 — ranked by |score change|). Returns per-market baseline vs scenario composite + component breakdown + the EXACT formula/weights in every response (transparent scenario_composite — deliberately NOT the DCPI). Keyless callers get a top-3 preview; any live key (claim_free_key) returns up to 25. Try: simulate_scenario avg_kwh_cents_pct=30 top_n=10. Do NOT use for the present-day ranking (use rank_markets) or trajectory extrapolation (use predict_market_trajectory); this answers explicit hypotheticals.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_nNoMarkets to return, ranked by |score delta| (default 10)
marketNoScore ONE market by slug (optional), e.g. abilene — slugs from rank_markets
avg_kwh_cents_pctNoPower price % change, e.g. 30 for +30% or -20 for -20%
curtailment_pct_deltaNoPercentage POINTS added/removed from curtailment
queue_wait_months_deltaNoMonths added/removed from interconnection queue wait
reserve_margin_pct_deltaNoPercentage POINTS added/removed from reserve margin, e.g. -5
time_to_power_months_deltaNoMonths added (+) or removed (-) from time-to-power, e.g. 12

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.
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.

Tool Definition Quality

A4.8/5.0
Behavior5/5

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

Annotations already indicate readOnly, idempotent, non-destructive. Description adds: returns baseline vs scenario composite + component breakdown + exact formula/weights, keyless preview vs full list, and exclusive data ownership. No contradictions.

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?

Structured with purpose first, then params, output, key behavior, example, exclusions. Slightly verbose but each sentence adds value. Could tighten some phrases without losing clarity.

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?

Covers all needed context: counterfactual use case, param details, output format (baseline vs scenario, transparent formula), key access tiers, exclusions. Output schema exists for return values, so no gaps.

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

Parameters4/5

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

Schema covers 100% with descriptions, but description adds context: explains delta meanings (e.g., percentage vs points), mentions 'pass at least one delta', clarifies top_n default/max and market as slug. Adds value beyond schema.

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?

Clearly states it is a counterfactual WHAT-IF re-scoring tool for 300+ DC Hub power markets, with explicit verb and resource. Distinguishes from siblings by specifying when NOT to use (rank_markets, predict_market_trajectory).

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 says when to use: for hypotheticals about market ranking changes. Provides exclusions: not for present-day ranking or trajectory extrapolation, with sibling tool names. Includes example call.

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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