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DC Hub — Data Center Site Selection & Colocation: Electricity, Power Grid, Gas, Fiber

by cloud.dchub

Rank Markets

rank_markets
Read-onlyIdempotent

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint; the description adds valuable behavioral context beyond that: ai_ready ranks by DCPI buildability rather than installed build-out, the caveat that built-out markets are frequently AVOID for new AI load, and how to chain results into get_market_dcpi_rank using 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.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the most critical routing decision and is well-organized, but it is long and duplicates information already present in the input schema (parameter list) and output schema (return fields). Some redundancy could be trimmed without losing value.

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?

Despite the length, the description is complete: it covers when to use, when not to use, an example, the ai_ready special case, the expected return shape, and downstream drilling via metro_slug. The output schema handles return details, so nothing essential is missing for an agent to invoke this tool 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 baseline is 3. The description adds useful semantics for criteria (especially ai_ready), an example mapping, and filter examples. However, it states limit is 1-50, while the schema allows up to 500, creating a misleading inconsistency that prevents a higher score.

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 states the tool returns a single ranked list across a 300+ market set, with a specific verb and resource. It strongly differentiates itself from siblings like execute_plan (siting decisions with verdicts), get_market_intel (deep one-market reads), and analyze_site (lat/lon scoring).

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 criteria ('top N markets for X'), an example query mapped to parameters, and a detailed when-not-to-use list covering execute_plan, get_market_intel, and analyze_site. It even explains the latency tradeoff against execute_plan.

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

A3.8/5.0
Disambiguation1/5

With 85 tools, several families are heavily overlapping: semantic_search and search_intelligence are explicitly documented as the same retrieval with different call shapes, save_site and save_to_shortlist both persist sites, and list_saved_sites and get_shortlist both read saved sites. Additionally, site scoring is split across analyze_site, get_composite_site_score, score_facility, and rank_sites, making correct tool selection very difficult for an agent.

Naming Consistency3/5

All names are snake_case and mostly readable, but conventions are mixed: many use get_* (get_facility, get_grid_intelligence), others use verb phrases (analyze_site, compare_isos, rank_markets), and some are bare noun phrases (ai_capacity_index, hyperscaler_deals, grid_transition_radar, site_selection_canvas). The search family alone uses search, search_facilities, semantic_search, and search_intelligence with no consistent pattern.

Tool Count1/5

85 tools is an extreme count for any MCP server, far beyond the 3-15 well-scoped range and above the 50+ threshold described as an extreme mismatch. Even with a wide domain like data-center siting, this many tools overwhelms agent context and makes selection costly.

Completeness4/5

The domain surface is exceptionally broad: siting, grid, gas, fiber, water, climate, tax, permitting, deals, news, facilities, saved shortlists, alerts, webhooks, key management, and research dossiers are all covered with connected workflows. Minor gaps exist, such as no delete or update for saved sites and no pause/resume for standing intents, but these are workable rather than blocking.

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