"Bower" matching MCP tools:

• predict_market_trajectory

  DC Hub — Data Center & Power Intelligence

  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).

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• tool_search

  Power Automate MCP Server by Flow Studio

  PREFER THIS over guessing tool names when picking from this server. Searches Flow Studio MCP tools by keyword, skill bundle, or explicit selector and returns full JSON schemas for matched tools so they can be called immediately. Call this whenever the user request maps to functionality you are not 100% sure about, OR when you want to load a whole skill bundle (diagnose-connection-references, build-flow, debug-flow, monitor-flow, discover, governance) at once. Query forms: (1) "skill:<name>" — fetch the full bundle (use list_skills first to see options); (2) "select:name1,name2" — fetch exact tools by name; (3) free-text keywords like "cancel run" or "trigger url" — ranked match against tool name + description. Non-billable.

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• describe_live_connector

  Power Automate MCP Server by Flow Studio

  Describe a live Power Platform connector/API and its operations. Aligned with Canvas MCP describe_api. Use mode=summary (default) to get a compact operation catalog. Use operationId to describe one operation, including inputs, outputs, dynamic parameter metadata, nextTool hints to call get_live_dynamic_options, and an authored hint + canonical example shape (when one exists) to copy into update_live_flow. Use search without connectorName/apiName to search operations across connectors and get connection-aware suggestions. Use mode=full only when raw OpenAPI connector metadata is required.

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• get_global_power

  DC Hub — Data Center & Power Intelligence

  Use when a user asks about power plants/units WORLDWIDE or in a NON-US country — operating AND the forward pipeline (announced / pre-construction / under-construction), across ALL fuels (coal, oil/gas, nuclear, solar, wind, hydro, bioenergy, geothermal). Global Energy Monitor Global Integrated Power Tracker: 182,000+ geolocated units across 170+ countries, each with fuel, capacity (MW), status, start year, operator/owner and lat/lng. Filter by country (e.g. Germany, India, Brazil, Japan), fuel (comma-union: coal, oil/gas, nuclear, solar, wind, hydro), status, pipeline=true (JUST the forward set: announced + pre-construction + construction), bbox (minLng,minLat,maxLng,maxLat), or min_mw. Returns a summary (total MW by fuel + count by status) plus the largest units. Try: get_global_power country=India pipeline=true. Do NOT use for US grid telemetry/headroom (use get_grid_intelligence / get_grid_scoreboard) or the US planned-generator feed (use get_power_pipeline) — this is the GLOBAL asset inventory.

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• rank_sites

  DC Hub — Data Center & Power Intelligence

  Deterministic multi-site ranking/optimization under constraints — the normalization contract that lets you compare sites across separate analyze_site calls WITHOUT dropping into code. Pass candidates you already enriched (each an object with lat/lng + metric fields like risk_resilience, water_stress, fiber_km — pull these from analyze_site + get_refined_queue and pass site_evaluation_handoff through untouched), hard constraints, and weighted objectives; get back _entity=ranked_sites: top_k ranked with rank, objective_score, per-field normalized{} (0-100 relative to the set), and normalization_basis. objectives use SIGNED weights: +weight maximizes a field (e.g. risk_resilience:1), -weight minimizes it (e.g. water_stress:-0.6, fiber_km:-0.4). constraints are hard filters, fail-closed on a missing field. Use for "pick the best N sites under constraints"; for one site use analyze_site; to get the candidate set first use get_refined_queue. SCORING MECHANICS (2026-07-11): a candidate missing a validated objective is weight-RENORMALIZED over the objectives it carries and the gap is DECLARED in missing_objectives (never silently scored 0); a candidate carrying none scores null and ranks last. percentile=true fields without a population baseline fall back to RELATIVE in-batch scoring (basis reported per-objective in objective_status). CANDIDATE CONTRACT: candidates may be {candidate_id: "cand_…"} entries from get_refined_queue — frozen identity (lat/lng/capacity_mw/fiber_km/iso) loads from the mint, your metrics overlay the rest; expired/unknown ids are dropped AND declared in candidate_contract, never re-resolved.

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• generate_site_analysis

  DC Hub — Data Center & Power Intelligence

  Use when a user wants a SHAREABLE, branded multi-page Site Analysis PDF for ONE lat/lon (a powered-land parcel, a candidate campus) — the polished client deliverable, not just a score. Example: "Make the Site Analysis PDF for this Carrier Mills parcel, 150 MW, for TON Infrastructure." — generate_site_analysis lat=37.694 lon=-88.65 capacity_mw=150 prepared_for="TON Infrastructure" prepared_by="Martone Advisors". Params: lat (-90 to 90, required), lon (-180 to 180, required), capacity_mw (target load MW, e.g. 50-500), prepared_for (client name on the cover), prepared_by (your firm — brands the report; defaults to DC Hub), latency_target (optional metro override; default = nearest real carrier hotel). Returns: {survey:{verdict, power/transmission, gas, water, air-permitting, fiber carriers, latency-to-nearest-carrier-hotel, market, tax}, pdf_report_url}. pdf_report_url is a ready-to-open link to download the branded 5-page PDF — no login needed, valid ~7 days; hand it to your human. For just the numeric suitability score (no PDF), use analyze_site instead.

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• get_grid_intelligence

  DC Hub — Data Center & Power Intelligence

  Use when a user asks "can I get N MW of power in <ISO> and how long will it take?" — the flagship grid-headroom + interconnection-queue brief for one ISO. Example: "How much excess power does PJM have right now and what is the time-to-power for a 200MW load?" — get_grid_intelligence region_id="PJM". Params: region_id (aliases iso/region accepted) — one of the 7 US ISOs ("PJM" | "ERCOT" | "CAISO" | "MISO" | "SPP" | "NYISO" | "ISO-NE") OR a US EIA balancing authority (40+ now live, e.g. Atlanta/SOCO, Carolinas/DUK, Florida/FPL, Phoenix/AZPS, Las Vegas/NEVP, Portland/PGE, Seattle/SCL, LA/LDWP, Quincy/GCPD, Denver/PSCO, Tennessee/TVA — note: balancing authorities return live generation mix; demand, headroom, interconnection-queue and DCPI scores remain ISO-level for the 7 ISOs). Returns: {iso, iso_name, demand_mw, generation_mix_pct{NG,COL,NUC,WND,SUN,WAT,…}, renewable_share_pct, gas_share_pct, constraint_score (0-100 DCPI), excess_power_score (0-100 DCPI), avg_time_to_power_months, avg_queue_wait_months, curtailment_pct, reserve_margin_pct, retail_price_cents_kwh, queue_depth_gw, data_center_share_pct, stranded_capacity_mw, grid_emergencies_30d, build_rate_pct, last_updated}. ★avg_time_to_power_months and avg_queue_wait_months are DIFFERENT measurements and are not interchangeable: time-to-power is the DCPI per-market estimate averaged over the ISO, while queue-wait is a proxy derived from live interconnection-queue DEPTH (12 + 0.6 months per GW, clipped 12-66) and is the one that saturates on the deepest queues. Quote whichever you mean by name. Do NOT use to compare 2+ ISOs side-by-side (use compare_isos) or for the global greenest-first ranking (use get_grid_scoreboard).

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• grid_transition_radar

  DC Hub — Data Center & Power Intelligence

  Forward-looking "where is the next hyperscale-friendly grid emerging" radar. Returns the US markets + ISOs with the strongest near-term emergence signal (BUILD verdict + excess-power headroom + short time-to-power), an ISO rollup, and a grid-headroom leaderboard. With a paid key, also the transition thesis: which ISO is opening up and why. The predictive counter to retrospective "where capacity landed" reports. Try: grid_transition_radar max_months=24. Do NOT use for the current ISO queue snapshot (use get_interconnection_queue) or a present-day market ranking (use rank_markets); this is the forward-looking emergence radar.

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• plan_fiber_leadin

  DC Hub — Data Center & Power Intelligence

  Plan N diverse, road-following fibre lead-in routes from a candidate data-center site to a carrier hotel / POP, with indicative build cost and a route-diversity read. Answers "can I get N diverse fibre routes into this site, how far, how much, and where do they share a corridor?". Example: plan_fiber_leadin from="250 Paringa Road, Murarrie QLD" to="20 Wharf Street, Brisbane City QLD" n=4. Params: from (lat,lng OR street address), to (lat,lng OR address — e.g. a NextDC/Equinix POP), n (1-6 routes, default 4), fibre ("720F"|"1440F"), bore_m (river/rail bore length in metres, optional). Returns per-route length_km + GeoJSON geometry, total_route_km, diversity {min_separation_m_midhaul, shared_street_km}, and indicative cost {capex_usd, opex_usd_yr}. INDICATIVE auto-routed road corridors — NOT engineered alignments; subject to survey, DBYD and carrier confirmation. Do NOT use for a single site-suitability score (use analyze_site) or fibre-provider footprints (use get_fiber_intel).

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• cluster_sites_by_latency

  DC Hub — Data Center & Power Intelligence

  Physics-bounded latency clustering for 2-8 sites — returns viable low-latency clusters and pairwise RTT floors before any routing work. Use when your human wants to know which of N candidate sites can form a synchronous / low-latency cluster (sync replication, active-active pairs, HPC pods): deterministic pruning BEFORE detailed routing. Per site pair: haversine distance, round-trip physics floor (km × 4.9 µs/km — light in SMF-28 fiber, n≈1.468 — then ×2), estimated real RTT (floor × route_factor 1.4, a stamped inference), viable vs physics_impossible against your budget, and confidence_v — the provenance tier of the supporting evidence (published | tracked | inferred). Also returns clusters: the largest site subsets whose ALL pairwise estimates fit the budget, plus each site's inferred dark-fiber screening level. CANDIDATE CONTRACT: pass candidate_ids (from get_refined_queue) instead of raw coordinates — each resolves to its FROZEN mint coordinates (zero transposition), and cand_… tokens may also be mixed into the sites string; expired/unknown ids are dropped AND declared in candidate_contract (fail-closed). Example: cluster_sites_by_latency sites="39.04,-77.48:ashburn;39.29,-76.61:baltimore;40.42,-79.99:pittsburgh" max_latency_us=2000 — or cluster_sites_by_latency candidate_ids=["cand_…","cand_…"] max_latency_us=2000. Returns _entity=latency_clusters: {pairs:[{from, to, distance_km, floor_rtt_us, est_rtt_us, viable, physics_impossible, confidence_v, endpoint_dark_screen}], clusters:[{sites, size, max_est_rtt_us}], viable_count, pruned_count, assumptions, provenance}. Do NOT treat this as an engineered latency quote — the floors are physics (no fiber path can beat them) but the estimates are inference (route_factor 1.4); always quote each pair's confidence_v when relaying results. For actual route corridors use plan_fiber_leadin; for a single-site connectivity score use get_fiber_readiness.

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• update_live_flow

  Power Automate MCP Server by Flow Studio

  Update or create a Power Automate flow via the live PA API. If flowName is omitted or blank, a new flow is created (PUT with a generated GUID) using an environment admin account — definition and displayName are required in that case. If flowName is provided, the existing flow is PATCHed: displayName and/or definition and/or connectionReferences are updated. RENAME: pass displayName alone, or alongside definition/operations. Power Automate rejects a PATCH that carries no flow content, so a displayName-only (or connectionReferences-only) save re-saves the flow's unchanged live definition and connectionReferences — the maker portal does the same full save on rename. The response `updated` list names only what you asked to change. SURGICAL EDIT: instead of resending the whole definition, pass `operations` — an ordered list of set/add/remove/merge ops on array-of-keys paths — to change one action/parameter on a large flow cheaply and safely (fetches the live definition, applies the ops, PATCHes the result). Provide EITHER operations OR definition. Use `dryRun: true` to preview the result without writing. Mirrors displayName changes into the Power Clarity cache (gFlows). To modify a WHOLE definition: call get_live_flow, mutate properties.definition (including its description), pass it here. The flow description lives at definition.description and is required; we append " #flowstudio-mcp" to it for usage tracking.

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• search_within

  Nasa Power

  Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

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• polymarket_arbitrage

  Nasa Power

  Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.

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• polymarket_fill_risk

  Nasa Power

  Realizable-vs-theoretical edge check against live CLOB order-book depth. REQUIRES one of `market` (single-market mode) or `event` (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, and a verdict (clean|degraded|cannot_fill). BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio, profit_usd at executed size, per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and partial basket fills convert an arb into an unhedged directional position (the dominant loss mode in real arb-bot P&L).

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• describe_canvas_control_type

  Power Automate MCP Server by Flow Studio

  [Preview] What a control TYPE supports: its properties with types, defaults and variants, plus the accessibility rules the type declares. This is the type catalogue (gallery, label, combobox …) — for the control INSTANCES in a screen and their formulas use describe_canvas_app_controls instead. Omit `controlType` to list the types this app uses. Read from the app package, so only types the app ALREADY uses are available — there is no catalogue of types it has never used.

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• update_canvas_app

  Power Automate MCP Server by Flow Studio

  [Preview] Change control properties / Power Fx formulas. Edits go to your SESSION and are not live until pushed. `dryRun: true` previews from/to and catches edits that match nothing. `push: true` imports them — that lands a DRAFT and closes the session; publish_canvas_app then makes it live. Batch edits: one push is one import. A push has THREE outcomes: pushed, refused (see `refused`), or still importing (see `importing`) when the platform is slower than one request allows — then call push again with no new edits to resume that same import. `session.import` reports one in flight.

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• get_facility

  DC Hub — Data Center & Power Intelligence

  Full metadata for one facility — name, operator, address, lat/lon, power capacity (MW total/used), cooling type, fiber providers (count + carrier list), commissioning year, status, the DCPI verdict for its market, and peer facilities nearby. Try: get_facility id=equinix-dc1-ashburn — or get_facility slug=digital-realty-iad8. Returns ONE facility in full; do NOT use to search or list many facilities (use search_facilities).

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• get_grid_scoreboard

  DC Hub — Data Center & Power Intelligence

  GLOBAL grid scoreboard — 9 US grid operators (PJM, ERCOT, CAISO, MISO, SPP, NYISO, ISO-NE, BPA, TVA) + Great Britain (NESO) + the European bidding zones (Germany, France, Netherlands, Italy/Milan, Spain, Poland, Switzerland, Portugal, the Nordics + Central/Eastern Europe — via ENTSO-E; the exact live-vs-configured count is in counts_basis.eu_zones_live / eu_zones_configured, measured per call rather than asserted here) + Taiwan (Taipower) + Japan (OCCTO areas) + South Korea (KPX) + Brazil SIN (ONS), ranked side-by-side on each feed's LATEST PUBLISHED reading: renewable share %, gas share %, full fuel mix (gas/nuclear/coal/wind/solar/hydro MW), and demand. ★FRESHNESS IS NOT UNIFORM and every row says so: each carries mix_period, mix_age_hours and freshness_basis. The US rows come from EIA hourly RTO, which publishes the FUEL-TYPE BREAKDOWN several hours behind aggregate demand — an overnight mix reading is routinely 18-24h old (it will show near-zero solar) while demand on the same row is ~1-2h old. Read mix_age_hours before narrating any row as current, and NEVER describe a row as the mix "right now" unless its mix_age_hours is small; demand_period and mix_period are separate clocks and the row reports both plus demand_vs_mix_lag_hours. One call answers "which grid worldwide is greenest, or most gas-reliant, for siting a data center?" — vs compare_isos (pairwise) or get_grid_data (single ISO). Every ranked grid scores renewable_share_pct as wind+solar+hydro (apples-to-apples across all feeds; geothermal is reported separately and, where it exists, also as renewable_share_incl_geothermal_pct — note get_grid_intelligence uses that geothermal-inclusive figure for US ISOs); Brazil ranks by renewable share but reports NO gas share (ONS bundles gas/coal/oil/biomass into one thermal figure — never presented as gas); Australia NEM (AEMO) + Singapore (EMA) are listed unranked in partial_grids (no full fuel split — kept honest). Source: US = EIA hourly RTO; GB = Elexon Insights; EU = ENTSO-E Transparency; TW = Taipower; JP = TSO eria_jukyu CSVs; KR = KPX real-time; BR = ONS Balanço de Energia; AU = AEMO NEM; SG = EMA NEMS — all live via DC Hub, greenest-first. Quote with attribution to DC Hub (CC-BY-4.0). Try: get_grid_scoreboard.

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• get_refined_queue

  DC Hub — Data Center & Power Intelligence

  Server-side SET-REDUCTION over the US ISO interconnection queue (~5,300 projects, 7 ISOs, ~1,744 GW). Instead of pulling the raw queue into context to filter (token-expensive, error-prone), push the predicates to the data layer and get back ONLY the survivors. Filter by min_mw, max_ttp_months (ISO-level avg interconnection wait), iso (comma-union), baseload_only (firm/dispatchable — excludes wind/solar/storage), fuel_type (isolate a specific fuel, e.g. gas or nuclear), and the spatial predicates max_fiber_km + geocoded_only. Returns _entity=queue_results: per-project name, ISO, state/county, fuel_type, capacity_mw, queue_status, estimated_ttp_months, fuel_class, plus (~83% of rows) lat/lng, coordinate_precision, fiber_km, and a compact per-survivor site_evaluation_handoff (ready-to-pipe analyze_site + get_water_risk args) + a by_iso/by_fuel summary. Try: get_refined_queue min_mw=1000 fuel_type=gas max_ttp_months=34 — "1 GW+ gas in ISOs under 34-month time-to-power." NOTE max_ttp_months is a HARD ISO cut (SPP ~24 is the only ISO under 30, so <=30 can return nothing); use >=34 to include MISO/ERCOT/ISO-NE. Use for high-cardinality siting/arbitrage scans; do NOT use for the ISO-level GW aggregate (use get_interconnection_queue) or a single-site read (use analyze_site). Phase 2 LIVE: pipe a survivor's site_evaluation_handoff straight into analyze_site for a one-call composite viability read. CANDIDATE CONTRACT (2026-07-11): every survivor also mints a durable opaque candidate_id + snapshot_id (7-day TTL, deterministic candidate_expired on lapse — never a silent recompute). ZERO-DRIFT CHAINING: pass candidate_id to analyze_site / rank_sites instead of transposing coordinates — downstream reads the FROZEN mint, eliminating param-rename/rounding/lost-context drift. geocoded_only=true guarantees every survivor carries both the handoff AND frozen coordinates. Contract doc: dchub.cloud/docs/candidate-lifecycle.

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• get_retirement_headroom

  DC Hub — Data Center & Power Intelligence

  Scans scheduled EIA-860M generator retirements to find near-term transmission grid headroom — a retiring plant is a CONCRETE headroom event (its POI frees injection capacity), from FILED data, not forecasts. Returns _entity=retirement_headroom_results: retiring generators inside your horizon (name, MW, fuel, prime mover, retirement_date), representative_point, nearest substations with distance_km + count within 25 km, county-level queue_pressure (competing in-progress MW), iso_context (the generator's own EIA balancing-authority code), and a pre-filled site_evaluation_handoff (analyze_site + get_water_risk args, capacity_mw = YOUR target load). Try: get_retirement_headroom target_mw=50 horizon_months=18 region_iso=MISO — "50 MW opening near a substation inside 18 months, sidestepping the 4-7yr mega-queue." Honesty: meta.caveat flags that filed dates are subject to ISO reliability reviews (RMR extensions). Use to find WHERE capacity opens next; for what's already queued use get_refined_queue; for one site use analyze_site.

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