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predict_market_trajectory

Read-onlyIdempotent

Forecast a DCPI market's near-term trajectory (1-8 quarters) to see if it trends toward BUILD or AVOID. Projects excess power and constraint scores with confidence bands that widen over time.

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
Behavior4/5

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

Annotations already indicate read-only and idempotent. Description adds honesty: linear extrapolation, widening bands, minimum data requirement. No contradiction.

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?

Well-structured and front-loaded, but slightly verbose. Every sentence adds value, though some redundancy with schema.

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?

Comprehensive: describes purpose, params, return shape, caveats, and exclusions. No output schema needed given detail.

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 already describes both parameters with 100% coverage. Description adds minor context (e.g., 2 quarters = ~6 months) but doesn't significantly surpass 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 forecasts DCPI market trajectory over 1-8 quarters, projecting scores with confidence bands. Distinguishes from siblings like get_market_dcpi_rank and rank_markets.

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 advises when to use (trend questions) and when not to use (single point-in-time, ranking). Provides specific alternative tool names.

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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