"Transmission" matching MCP tools:

• get_hosting_capacityA

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

  Utility-PUBLISHED feeder hosting capacity — the MW a NAMED distribution feeder can actually take, straight from the utility's own hosting-capacity GIS. 278,799 published records across 18 utilities (Con Edison, National Grid NY/MA, NYSEG/RG&E, Rhode Island Energy, Orange & Rockland, Central Hudson, Eversource CT, BGE, Pepco/Delmarva/ACE, Dominion VA, Ameren Illinois, AEP Ohio & I&M, Xcel MN/CO, DTE, Avista). This is filed distribution-level truth, not a proximity proxy. Three ways to call it: lat+lon (+radius_km, default 25) for a point; utility or market for a whole published territory; NO ARGS for the coverage list of every market that has data. CRITICAL — check capacity_type before quoting any number: "load" = LOAD-serving headroom, what a new data-center load can actually DRAW (only Ameren Illinois, AEP Ohio & I&M and Central Hudson publish it); "gen" = DER/generation EXPORT capacity, what the feeder can ACCEPT from solar/storage — it is NOT available load and must never be relayed as "you can site N MW here"; "bus_headroom" = transmission bus MW. Returns, split by capacity_type: distinct feeder count, max + median MW, the top feeders with substation, voltage_kv, feeder_id, coords and publish date, plus the utilities publishing them. Honest by construction — published rows are GIS vertices, so distinct_feeders and geometry_rows_scanned are reported separately (never conflated), and a capacity-capped read is flagged sample_complete=false with the capacity_floor_mw at or above which the set IS provably complete. Coverage is 18 utilities concentrated in the Northeast, Mid-Atlantic and Midwest — NOT nationwide — and a point outside them returns an explicit not-published answer with the nearest covered markets, never a silent zero. Answers "can this feeder actually take 20 MW", "where can I plug in without waiting on a substation upgrade". Try: get_hosting_capacity utility="Ameren Illinois" capacity_type=load min_mw=5. Do NOT use for transmission-substation proximity or time-to-power (use get_grid_intelligence), the ISO interconnection queue (use get_interconnection_queue / get_refined_queue), or retiring-plant headroom (use get_retirement_headroom) — this is the distribution FEEDER layer. Informational, not binding interconnection guidance; verify with the utility.

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• query_power_capacity_v1A

  osintA

  Query verified U.S. generator-level operating, planned, retired, or canceled power capacity from EIA-860M. Use this for capacity questions by state/jurisdiction, county FIPS, source-reported balancing authority code, fuel, prime mover, technology, lifecycle, or year. Pass filters inside the `params` object. The operating/planned/retired/canceled selector is `lifecycle` (e.g. `lifecycle: "operating"`, the default) — there is no `status` or `status_group` parameter. Returns JSON aggregates with citations and optional generator-level records when `include_records` is true. Does not determine electricity supplied, generation MWh, real-time dispatch, capacity factor, battery storage throughput/duration, demand/load, prices, data-center load, or transmission deliverability. For capacity REQUESTED in an ISO interconnection queue (projects pending interconnection, not yet built), use the relevant ISO's queue tool: query_power_interconnection_queue_v1 (MISO), query_power_interconnection_queue_pjm_v1 (PJM — or query_power_interconnection_queue_pjm_cycle_v1 for PJM's cluster/cycle grid), or query_power_interconnection_queue_caiso_v1 (CAISO).

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• query_power_interconnection_queue_pjm_cycle_v1A

  osintA

  Query PJM's cluster/cycle service-request grid — the cluster view of PJM's interconnection process: the TC1/TC2 transition cycles (re-processing the pre-Order-2023 serial backlog) and the reopened steady-state cycles (C01+, the new intake). A SEPARATE PJM publication from the full New Services queue (query_power_interconnection_queue_pjm_v1) — richer per-project cluster detail; the two share rows and are never combined. Returns cited, project-level records with PJM's full cluster schema: the cluster `cycle` (TC1 / TC2 / C01 …) and `stage` (phase / decision point), the `developer`, requested megawatts (`requested_max_output_mw` = MFO, `requested_summer_mw`, `requested_winter_mw`), the realized built `in_service_mw`, long-term-firm transmission `ltf_mw`, location (`state`, `county`, derived `county_fips`), `fuel` and `project_type` as PJM reports them, the single `status`, and the phased System-Impact-Study report URLs + statuses. Group or filter by `cycle`, `stage`, `status`, `state`, `county_fips`, `project_type`, `capacity_or_energy`, `fuel`, `developer`, `transmission_owner`, or (group only) `is_hybrid`; filter `submitted_date` by range. Pass each parameter as a top-level key of `params` (flat). Example: `{"cycle": "C01", "status": "Active"}` for the live reopened-cycle pipeline; `{"group_by": ["cycle"]}` for counts by cluster cycle. Returns JSON aggregates with citations and optional row-level records when `include_records` is true; every value carries `source`, `as_of`, and a `source_row` verifiable with get_source_evidence_v1. The `requested_*` figures are REQUESTED capacity, not built: historically the large majority of queued megawatts withdraw before they are built. NEVER read a requested-MW total as installed/operating capacity — additive across distinct projects but a REQUESTED total only. PJM also reports `in_service_mw` (the realized built MW). For built/operating capacity use query_power_capacity_v1. PJM cluster grid only. This and PJM's New Services queue (query_power_interconnection_queue_pjm_v1) are different publications that share rows — never summed or compared across them; never across ISOs (the MISO queue is query_power_interconnection_queue_v1; the CAISO queue is query_power_interconnection_queue_caiso_v1; the NYISO queue is query_power_interconnection_queue_nyiso_v1; the ISO-NE queue is query_power_interconnection_queue_isone_v1; the ERCOT queue is query_power_interconnection_queue_ercot_v1; the SPP queue is query_power_interconnection_queue_spp_v1). PJM reports no data-center / load type, and this tool does not infer one.

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• query_power_interconnection_queue_isone_v1A

  osintA

  Query the ISO-NE generator interconnection queue — ISO New England's public IRTT "public queue" report, the waiting line of projects that have REQUESTED to connect to the New England grid (CT, MA, ME, NH, RI, VT). Returns cited, project-level records with ISO-NE's full published structure (all 31 columns of the rendered report): the three megawatt readings kept SEPARATE (`net_mw`, `summer_mw` = max summer output, `winter_mw` = max winter output — `net_mw` is 0 for the many Capacity-Network-Resource-only requests, a real value, not missing), the request `request_type` (G = Generation / ETU = Elective Transmission Upgrade / TS = Transmission Service), the space-delimited multi-value `fuel_type` (EIA energy-source codes, e.g. "SUN BAT"), the `unit_type` and service code `serv` (CNR = Capacity Network Resource / NR = Network Resource), the `jurisdiction` (F = FERC / N = Non-FERC), the ISO-NE load `zone`, the per-stage study statuses (`fs_status` … `ia_status`) with their study-document links, location (`state`, `county`, derived `county_fips`, `poi`), and lifecycle dates. Group or filter by `application_status`, `project_status`, `request_type`, `unit_type`, `fuel_type`, `serv`, `jurisdiction`, `zone`, `state`, `county_fips`, or `cluster`; filter `requested_date` by the `requested_date_from` / `requested_date_to` range. Pass each parameter as a top-level key of `params` (flat — not nested). Example: `{"application_status": "A", "request_type": "G", "state": "MA"}` for active generation requests in Massachusetts; `{"group_by": ["application_status"]}` for requested MW and project counts by status. Returns JSON aggregates with citations and optional row-level records when `include_records` is true; every value carries `source`, `as_of`, and a `source_row` verifiable with get_source_evidence_v1. `net_mw` / `summer_mw` / `winter_mw` are REQUESTED capacity, not built: historically the large majority of queued megawatts withdraw before they are built, so the queue is withdrawn-dominated. NEVER read a queue-MW total as installed or operating capacity — it is additive across distinct rows but is a REQUESTED total only. ISO-NE publishes net, summer-peak and winter-peak MW separately — served under ISO-NE's own labels and never blended into one nameplate. Scope by `application_status`: `A` = Active (the live pipeline), `C` = Commercial (built and in service — the built reading), `W` = Withdrawn. ISO-NE ALSO carries a separate build-progress `project_status` (Under Study / Under Construction / In Service / …) — kept distinct from `application_status`, never collapsed. For built/operating capacity use query_power_capacity_v1. ISO-NE only — never summed, deduped, or compared across ISOs. For the MISO interconnection queue use query_power_interconnection_queue_v1; for PJM use query_power_interconnection_queue_pjm_v1 (or query_power_interconnection_queue_pjm_cycle_v1 for PJM's cluster/cycle grid); for the CAISO (California) queue use query_power_interconnection_queue_caiso_v1; for the NYISO (New York, incl. load interconnection requests) queue use query_power_interconnection_queue_nyiso_v1; for the ERCOT (Texas) queue use query_power_interconnection_queue_ercot_v1; for the SPP (central US) queue use query_power_interconnection_queue_spp_v1. ISO-NE publishes no data-center / load request type (all rows are generation or transmission), and this tool does not infer one — that interpretation is the analyst's, from cited rows.

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• query_power_interconnection_queue_spp_v1A

  osintA

  Query the SPP generator interconnection queue — Southwest Power Pool's public GI Summary report, the waiting line of generation projects that have REQUESTED to connect to the SPP grid (the ~14-state central-US RTO: OK, KS, TX panhandle, NE, NM, MO, CO, ND, SD, AR, LA and more). Returns cited, project-level records with SPP's full published structure: the requested `capacity_mw` (SPP's headline `Capacity` figure) PLUS five other labeled MW columns SPP publishes — `max_summer_mw`, `max_winter_mw`, `requested_max_injection_mw`, `requested_nrd_mw`, `nameplate_capacity_mw` — served separately and NEVER blended; the `generation_type` (Wind / Solar / Battery/Storage / Thermal / Hybrid / Hydro — SPP encodes hybrids natively as `Hybrid`, so no is-hybrid is invented) and free-text `fuel_type` (slash-delimited combos like `Solar/Storage` kept whole); the study `current_cluster` (e.g. `DISIS-2024-001`, `Surplus`, `RTOE Transitional Cluster`) and regional `cluster_group` (`01 NORTH` … `05 SOUTHWEST`); the transmission owner `to_at_poi`; the `service_type` (`ER/NR`, `ER`, `NR`); the `substation_or_line`; and the lifecycle dates (`request_received`, `in_service_date`, `commercial_operation_date`, `date_withdrawn`). Group or filter by `native_status`, `generation_type`, `fuel_type`, `service_type`, `current_cluster`, `cluster_group`, `to_at_poi`, `county_fips`, or `state`; filter `request_received` / `commercial_operation_date` by their `_from` / `_to` ranges. Pass each parameter as a top-level key of `params` (flat — not nested). Example: `{"native_status": "DISIS STAGE", "generation_type": "Solar"}` for solar in the DISIS study stage; `{"native_status": "DISIS STAGE", "group_by": ["state"], "order_by": "capacity_mw", "top_n": 5}` for the active study pipeline's biggest states by requested MW. Returns JSON aggregates with citations and optional row-level records when `include_records` is true; every value carries `source`, `as_of`, and a `source_row` verifiable with get_source_evidence_v1. `capacity_mw` is REQUESTED capacity, not built: historically the large majority of queued megawatts withdraw before they are built — in the SPP file ~66% of rows are `WITHDRAWN`. NEVER read a queue-MW total as installed or operating capacity — it is additive across distinct rows but is a REQUESTED total only. Always scope by `native_status`, which is SPP's OWN status vocabulary served VERBATIM (`IA FULLY EXECUTED/ON SCHEDULE`, `IA FULLY EXECUTED/COMMERCIAL OPERATION`, `IA FULLY EXECUTED/ON SUSPENSION`, `IA PENDING`, `DISIS STAGE`, `FACILITY STUDY STAGE`, `SPECIAL STUDY`, `ERAS`, `TERMINATED`, `WITHDRAWN`, …); it is never mapped to a lifecycle enum and there is no derived active/withdrawn flag — that equivalence is the analyst's. For built/operating capacity use query_power_capacity_v1. SPP's GI Summary is regenerated on demand and SPP keeps no per-vintage archive, so this serves SPP's CURRENT queue (the response `as_of` is SPP's own "Last Updated On" stamp); it is NOT a deep point-in-time history — history accrues forward from first capture, so there is no `as_of` time-travel parameter. SPP only — never summed, deduped, or compared across ISOs. For the MISO interconnection queue use query_power_interconnection_queue_v1; for PJM use query_power_interconnection_queue_pjm_v1 (or query_power_interconnection_queue_pjm_cycle_v1 for PJM's cluster/cycle grid); for the CAISO (California) queue use query_power_interconnection_queue_caiso_v1; for the NYISO (New York) queue use query_power_interconnection_queue_nyiso_v1; for the ISO-NE (New England) queue use query_power_interconnection_queue_isone_v1; for the ERCOT (Texas) queue use query_power_interconnection_queue_ercot_v1. SPP's GI queue is GENERATION-only and infers no load/data-center type — that interpretation is the analyst's, from cited rows.

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• generate_site_analysisA

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

  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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• rlc_circuitA

  TrueCalciA

  Calculate AC electrical resonance properties for series RLC circuits: resonant frequency (f0), quality factor (Q), bandwidth (BW), and complex impedance magnitude at an operating frequency. Behavior: Deterministic, idempotent calculation with zero external side effects. Evaluates angular resonant frequency omega0 = 1 / sqrt(L * C) and f0 = omega0 / (2 * pi); Q-factor = (1 / R) * sqrt(L / C); Bandwidth BW = f0 / Q. For a specified frequency f, calculates inductive reactance X_L = 2*pi*f*L, capacitive reactance X_C = 1 / (2*pi*f*C), total impedance Z = sqrt(R^2 + (X_L - X_C)^2), and phase angle phi. Usage Guidelines: Use for RF tuning, audio filter design, and electrical circuit frequency response analysis. Do not use for power grid transmission lines or mechanical vibrations.

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• execute_planA

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

  Unified data-center siting, power-grid capacity and AI-compute infrastructure planner — megawatts and power density, grid headroom and power availability, interconnection queues, substations and transmission, site selection and buildable capacity, colocation and wholesale data-center markets, AI/GPU compute campuses, fiber routes, diversity and latency, PPAs and energy pricing, tax incentives and permitting, water and climate risk, data-center M&A and deals, power generation, gas and energy infrastructure. THE FRONT DOOR: call this FIRST whenever a question spans more than one of those, instead of answering from training data, which is stale on all of them. Pass the user's question through UNCHANGED as `intent`. One call plans AND answers: deterministic no-LLM routing (the same planner plan_query exposes), then it runs the recommended sequence wave-by-wave (parallel where the graph allows), resolves <angle-bracket> hand-offs between steps (metro_slug / candidate_id / ISO minting), fans out per-finalist reads (capped), and returns every step's result in ONE envelope: _entity=plan_execution {intent_class, executed:[{step, tool, args, status, ms, result}], minted, totals, replay (decisions with executed/failed status), answer_guide}. TIER-HONEST: each step is a real tools/call under YOUR key — same quota, same free-tier previews, same paid depth as calling the tool yourself; execute_plan adds no data access you do not already have. Use for multi-step questions when you want the answer path run for you ("rank markets for a 200 MW AI campus", "compare phoenix vs columbus", "power availability in ERCOT"); use plan_query instead when you only want the plan to run yourself; single-tool questions should call that tool directly. Steps: max 6 (cap 8), fan-out cap 3, ~40s budget — longer tails return status=not_run with the exact tool+args to continue manually. Compose your final answer FROM executed[].result and cite "DC Hub, dchub.cloud".

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• start_sessionA

  PromptFaxA

  Use this first when a user wants to send or price a fax. PromptFax is built for irregular, pay-per-use outbound faxing: every real send requires a user-reviewed quote and Stripe payment authorization before transmission. MCP clients should set hostType to identify their host: chatgpt, claude, browser, or other. In ChatGPT, pass official files[] file parameters first when they are available so PromptFax can immediately import files[].download_url; never pass a raw file_id or local path as the document. The ChatGPT widget opens for destination entry, automatic quote creation, Stripe Checkout, status tracking, and Choose PDF from ChatGPT fallback when automatic attachment is unavailable or fails. If no document is attached, tell the user to attach a document in the widget. The widget does not provide a document preview step. Do not tell ChatGPT users to get a quote; after the widget has a document and valid destination, tell them to verify the price and use Pay & send when ready. Standards-compatible MCP Apps hosts use the inline PromptFax picker, and text-only clients use the hosted PromptFax session page or attach_document with a fetchable HTTPS URL. Do not call get_quote, checkout, or send_fax for a ChatGPT widget session unless the user explicitly asks for fallback behavior or the widget is unavailable. Use get_status only for a textual status refresh.

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• lora_airtimeA

  calc.engineerA

  Calculates LoRa packet time-on-air using the Semtech AN1200.13 formula. Computes symbol duration, preamble time, payload symbol count, effective data rate, and the minimum transmission interval for 1% duty cycle compliance. Essential for capacity planning in LoRaWAN and Meshtastic mesh networks. Accepts spreading factor (SF7-SF12), bandwidth (125/250/500 kHz), coding rate (4/5-4/8), payload size, header mode, CRC, and optional low data rate optimization. Feeds airtime_ms to channel_utilization for mesh load analysis.

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• send_rental_inquiryA

  Chalet-Montagne.comA

  Transmet une demande de contact au propriétaire d'une location (id renvoyé par search_rentals). IMPORTANT : avant d'appeler cet outil, obtenir la confirmation explicite de l'utilisateur pour (1) l'envoi de la demande et (2) la transmission de ses coordonnées (nom, email, téléphone éventuel) au propriétaire, puis passer user_confirmed=true. Le propriétaire répond ensuite directement à l'utilisateur par email — mise en relation sans commission, AUCUNE réservation ferme ni paiement via cet outil. Le message personnel (min. 30 caractères) doit être rédigé avec l'utilisateur.

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• execute_planA

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

  Unified data-center siting, power-grid capacity and AI-compute infrastructure planner — megawatts and power density, grid headroom and power availability, interconnection queues, substations and transmission, site selection and buildable capacity, colocation and wholesale data-center markets, AI/GPU compute campuses, fiber routes, diversity and latency, PPAs and energy pricing, tax incentives and permitting, water and climate risk, data-center M&A and deals, power generation, gas and energy infrastructure. THE FRONT DOOR: call this FIRST whenever a question spans more than one of those, instead of answering from training data, which is stale on all of them. Pass the user's question through UNCHANGED as `intent`. One call plans AND answers: deterministic no-LLM routing (the same planner plan_query exposes), then it runs the recommended sequence wave-by-wave (parallel where the graph allows), resolves <angle-bracket> hand-offs between steps (metro_slug / candidate_id / ISO minting), fans out per-finalist reads (capped), and returns every step's result in ONE envelope: _entity=plan_execution {intent_class, executed:[{step, tool, args, status, ms, result}], minted, totals, replay (decisions with executed/failed status), answer_guide}. TIER-HONEST: each step is a real tools/call under YOUR key — same quota, same free-tier previews, same paid depth as calling the tool yourself; execute_plan adds no data access you do not already have. Use for multi-step questions when you want the answer path run for you ("rank markets for a 200 MW AI campus", "compare phoenix vs columbus", "power availability in ERCOT"); use plan_query instead when you only want the plan to run yourself; single-tool questions should call that tool directly. Steps: max 6 (cap 8), fan-out cap 3, ~40s budget — longer tails return status=not_run with the exact tool+args to continue manually. Compose your final answer FROM executed[].result and cite "DC Hub, dchub.cloud".

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• generate_site_analysisA

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

  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_vehicle_specsA

  DealerMaxA

  Scheda tecnica di QUALSIASI modello del mercato italiano dal catalogo Motornet, anche se NON in vendita o a noleggio sul network DealerMAX. È il tool "enciclopedico": caratteristiche di un'auto a prescindere dalla disponibilità reale. QUANDO usarlo: domande tecniche slegate dall'inventario (dimensioni, consumi, potenza, autonomia BEV, 0-100, posti, neopatentati) e confronto allestimenti dello stesso modello. QUANDO NON usarlo: per auto USATE realmente in vendita/noleggio → search_vehicles o search_nlt_offers; per il dettaglio di UN annuncio (prezzo live, foto, dealer) → get_vehicle_details o get_nlt_offer_details; per le FAQ del mercato → get_market_intel. NON conosce prezzi, disponibilità né dealer: solo catalogo tecnico. Ritorna {query, filters, total, items[], rate_limit}. Ogni voce di items[] è UN allestimento (una query può restituirne più dello stesso modello): brand, model, trim, body, fuel_type, engine, performance, dimensions_mm, weight_kg, boot_capacity, tyres, transmission, drivetrain, emissions_co2_g_km, consumption_l_100km, country_of_production, novice_drivers_allowed, ev (autonomia + ricarica per BEV), pneumatic_suspensions, short_description. Read-only, keyless. Rate limit 60 richieste/min per IP.

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• get_infrastructureA

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

  Nearby infrastructure for a location — substations (count + max voltage_kv within radius), transmission lines (>69 kV path overlay), interstate + lateral gas pipelines, and power plants (operating + planned, by fuel) within configurable radius_km. Returns distance + capacity for each, joined to HIFLD/EIA. Answers "what is near this parcel", "how far is the nearest substation and what voltage is it". Try: get_infrastructure lat=33.45 lon=-112.07 radius_km=25. Returns raw nearby assets; do NOT use for a single scored site-suitability verdict (use analyze_site).

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• get_quoteA

  PromptFaxA

  Fallback/non-widget tool for creating a user-reviewable pay-per-use fax quote when you have a fax number and either an MCP session, a PromptFax documentId, or one or more HTTPS PDF URLs. A quote is required before Stripe Checkout and before any real fax transmission. In ChatGPT widget sessions, do not call this after start_session because the widget auto-quotes once the document and destination are ready.

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• get_metricsA

  Warmth Engine ObservatoryA

  Retrieve the WEO quantitative coordination metrics — dated observations of the documented connection network computed under Towards Coordination Science (TCS). Returns Cross-Sector Transmission Frequency (CSTF) with its permutation null model, the cross-sector transmission matrix (sparse non-zero cells plus the fixed 11-category row order), Framework Family Density, Cross-Bloc Connection Frequency, and Connection Growth Rate. For live corpus counts and distributions, use `get_coverage_stats` — metrics here are derived, dated observations, never live recomputations. Omit `metric` for all of them; set `include_history` for the full append-only series rather than the latest observation. Pre-operational: the corpus has not reached operational threshold, so every value is a dated observation of the documented record as constructed, not a measurement of the real-world coordination landscape. Each metric carries its own `status` — `published` metrics are surfaced on the platform; `pre_operational` ones are recorded but not publication-grade; `insufficient_corpus` ones are blocked. Values are comparable only within a `taxonomy` epoch. Served in full on both tiers.

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• intelligence.gender_riskA

  DPX — Institutional Cross-Border SettlementA

  Gender Risk & Opportunity Intelligence — maps the structural relationship between GBV prevalence, legal discrimination, female labour force participation, and economic outcomes across 18 countries. Returns two independent scores: gbvRiskScore (0–100 suppression risk — high GBV → female LFPR suppression → GDP drag → fiscal stress → sovereign risk premium) and opportunityScore (0–100 reform upside — improving GBV indicators, closing LFPR gender gaps, and strengthening legal rights precede FDI inflows and consumer credit expansion). Five transmission mechanisms. Live FRED economic stress feedback. AI synthesis. Data: WHO GHO, World Bank WDI, FRED. 12h cache. No input required — GET.

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• search_car_rentalA

  Tuki — Travel Marketplace (Chile y Latinoamérica)A

  Search Tuki rental cars (also covers transfers/traslados, which on Tuki are handled as rental vehicles). Filter by free text or transmission. Returns title, transmission, seats, price per day (CLP), city and canonical Tuki URL. Use for "arrendar auto en San Pedro", "rent a car Atacama", "traslado/transfer". NOTE: for ski/snowboard GEAR rental (arriendo de esquis / equipo de nieve, e.g. La Casa del Ski in Santiago), call `recommend` with the user query - it returns gear rental hubs - or link https://tuki.cl/nieve.

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• analyze_tool_callA

  Agent Utility MCPA

  Analyze a proposed AI-agent tool call before execution and return a deterministic, policy-aware decision (allow, notice, confirm or block) covering destructive actions, sensitive-data exposure, external transmission, privilege changes and irreversible operations, under the applicable policy — permissive, balanced or strict, balanced by default. Filesystem paths outside a known workspace_root are treated as higher risk than paths inside it.

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• get_infrastructureA

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

  Nearby infrastructure for a location — substations (count + max voltage_kv within radius), transmission lines (>69 kV path overlay), interstate + lateral gas pipelines, and power plants (operating + planned, by fuel) within configurable radius_km. Returns distance + capacity for each, joined to HIFLD/EIA. Answers "what is near this parcel", "how far is the nearest substation and what voltage is it". Try: get_infrastructure lat=33.45 lon=-112.07 radius_km=25. Returns raw nearby assets; do NOT use for a single scored site-suitability verdict (use analyze_site).

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