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azmartone67

DC Hub — Data Center & Energy Intelligence

Predict Market Trajectory

predict_market_trajectory
Read-onlyIdempotent

Forecast a market's power trajectory over the next 1–8 quarters, projecting excess power and constraint scores with widening confidence bands to identify BUILD vs AVOID trends.

Instructions

Forecast a DCPI market's near-term trajectory (next 1-8 quarters). Projects excess_power_score and constraint_score forward with confidence bands that WIDEN with horizon, from DC Hub's daily DCPI snapshot history — the only source that can, because it owns the time-series. Use to answer "is this market trending toward BUILD or AVOID?" or "will Dallas power stay tight over the next 6 months?". Params: market_slug (required, metro slug e.g. dallas, phoenix, northern-virginia — valid slugs come from rank_markets / get_market_dcpi_rank); horizon_quarters (optional 1-8, default 4; 2 = ~6 months out). Returns {market_slug, method, basis{history_points, history_span_days, slope_per_day, trend}, horizon_quarters, projection[{quarter_out, excess_power_score, excess_power_band, constraint_score, constraint_band}], caveat, snapshot_record}. HONEST: linear trend extrapolation, NOT a guarantee — bands widen with horizon and short history; needs >=3 daily snapshots or it declines. Do NOT use for a single point-in-time verdict (use get_market_dcpi_rank) or to rank many markets (use rank_markets).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
market_slugNoMarket slug (metro), e.g. dallas, phoenix, northern-virginia — valid slugs come from rank_markets / get_market_dcpi_rank
horizon_quartersNoForecast horizon in quarters (1-8, default 4); 2 = ~6 months ahead

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.
Behavior5/5

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

Beyond the annotations (read-only, idempotent), the description discloses the method (linear extrapolation), the limitation (not a guarantee, bands widen with horizon/short history), and a data prerequisite (needs >=3 snapshots or it declines). It also names the data source and includes a caveat field in the output.

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 dense but every sentence earns its place: core action, use cases, parameter summary, return shape, honest caveat, and exclusion/alternatives. It is a single paragraph but logically ordered and front-loaded, with no filler.

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 (forecasting, projections, bands), the description covers the data source, method, limitations, prerequisite, and output structure. The presence of an output schema is supplemented by an explicit return object description, making it comprehensive for an agent to decide invocation.

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?

While the schema already describes both parameters in detail, the description flags market_slug as required (not indicated in the schema) and clarifies horizon_quarters' default and time mapping. It adds the 'required' semantic that is missing from the structured schema, but otherwise mostly reiterates schema content.

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 ('Forecast') and resource ('DCPI market's near-term trajectory'), then details the projected scores and confidence bands. It explicitly contrasts with sibling tools by stating what it is not for (single point-in-time or ranking), distinguishing it clearly.

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 gives concrete use cases ('is this market trending toward BUILD or AVOID?') and provides explicit alternative tools for other scenarios ('use get_market_dcpi_rank', 'use rank_markets'). It also tells where to get valid market_slug values, covering prerequisites.

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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