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azmartone67

DC Hub — Data Center & Energy Intelligence

Rank Markets

rank_markets
Read-onlyIdempotent

Rank data center markets by power price, capacity, growth, or AI-readiness to identify where new data center capacity should go.

Instructions

FRONT DOOR CHECK — if the question is "WHERE SHOULD I PUT MW" (a siting decision), call execute_plan(intent="<the user's question, unchanged>") instead: ONE call runs the market ranking AND the per-finalist BUILD/CAUTION/AVOID verdict AND the grid reality-check, and returns a replay naming the markets it rejected and why. If the question is "RANK MARKETS BY " — you want the ranked list itself and nothing attached — rank_markets IS the right call: stay here. The trade is real and runs the other way: execute_plan spent ~3 steps and roughly 4x this tool's latency on a measured market-ranking intent, so a single-capability ask should NOT be routed through the planner. Use when a user wants "the top N markets for X" — one ranked list across the 300+ market set rather than N separate get_market_intel calls. Example: "What are the 10 fastest-growing US markets with at least 100MW of existing capacity?" — rank_markets criteria=fastest_growing region=us limit=10 min_capacity_mw=100. Params: criteria one of "cheapest_power" | "most_capacity" | "most_operators" | "fastest_growing" | "best_overall" (default best_overall) | "ai_ready"; region one of "global" | "us" | "canada" | "eu" | "apac" | "americas" (default us); limit 1-50 (default 10); min_capacity_mw filter floor (e.g. 100). ★ criteria="ai_ready" ranks by DCPI BUILDABILITY (excess-power + time-to-power + BUILD/CAUTION/AVOID verdict) — where NEW AI-campus load can actually LAND — NOT by installed build-out (the other five criteria). Use ai_ready for AI/GPU/hyperscale campus siting: the most-built-out markets are frequently AVOID for new load, so a build-out ranking mis-answers "where do I put a 200MW AI campus". Returns: {criteria, region, result_count, results:[{rank, metro_slug, market, city, state, country, score, value, total_mw, facility_count, operator_count, url}], data_source, methodology}. To drill into a ranked market, feed results[].metro_slug into get_market_dcpi_rank. Do NOT use for a deep read on ONE market (use get_market_intel), for scoring a specific lat/lon (use analyze_site), or for a siting question that also needs the verdict and grid check attached (use execute_plan).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of markets to return, 1-50 (default 10)
fieldsNoReturn ONLY these row fields (array or comma string) — a token diet. The response envelope (citation, provenance, as_of, coverage, request_interpretation, the human relay line) is NEVER projected away; a projection narrows ROWS only.
regionNoRegion scope: "global", "us" (default), "canada", "eu", "apac", or "americas"
criteriaNoRanking criterion: "cheapest_power", "most_capacity", "most_operators", "fastest_growing", "best_overall" (default), or "ai_ready" (DCPI buildability — where new AI load can land, for AI-campus siting; region us/global)
projectionNoNamed field preset, cheaper to send than a field list: market_summary (ranking rows), siting_summary (site/point rows), identity_only (ids + names).
min_capacity_mwNoMinimum existing capacity filter in megawatts (MW), e.g. 100

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

A4.9/5.0
Behavior5/5

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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds valuable context beyond those hints, especially the critical ai_ready semantics: it ranks by DCPI buildability rather than installed capacity, and warns that highly built-out markets may be AVOID for new AI load. It also explains the return envelope and the follow-up use of metro_slug.

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 long but well-structured, with routing directives front-loaded, followed by usage, example, parameter reference, return shape, and explicit exclusions. A few points are repeated, such as the contrast with execute_plan, but every sentence carries useful decision-relevant 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?

Given the large sibling set and the high risk of confusing this tool with execute_plan, get_market_intel, or analyze_site, the description covers all necessary context: when to use, when not to use, how to invoke, what the output looks like, and how to continue the workflow via get_market_dcpi_rank. The ai_ready caveat prevents a costly misinterpretation.

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 goes further: it enumerates allowed criteria values with meanings, states defaults for criteria, region, and limit, gives a practical limit range of 1-50, and shows a concrete example with parameter assignments. The deep explanation of the ai_ready criterion adds meaning the schema alone does not convey.

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 clearly identifies the tool as producing 'one ranked list across the 300+ market set' and explicitly says 'rank_markets IS the right call' for list-only ranking requests. It distinguishes the tool from execute_plan, get_market_intel, and analyze_site, making its purpose unmistakable.

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 detailed when-to-use and when-not-to-use guidance: siting questions with verdicts/grid checks go to execute_plan, single-market deep reads go to get_market_intel, lat/lon scoring goes to analyze_site, and pure ranking stays here. This explicit exclusion list leaves no ambiguity about routing.

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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