"APM Terminals" matching MCP tools:

• book_meetgreetA

  bookingA

  Create a secure payment link for a VIP Meet & Greet booking, with optional transfer combo. Available locations: CDG, Orly, Gare de Lyon — Meet & Greet is NO LONGER offered at Gare du Nord (do not quote or book it there). M&G prices are FIXED (CDG/Orly 250€ base for 2 pax + 50€/extra, Gare de Lyon 350€ + 75€/extra) — do NOT call get_quote for the M&G part. Some event dates carry a supplement (e.g. Paris Air Show): the mg_price returned here is the amount actually charged for pickup_date. A connection (service_type='connection': arrival flight → connecting departure flight with fast-track between terminals) is billed as TWO M&G services = the location price ×2 (e.g. CDG 2 pax = 500€). For a combo with a transfer (e.g. CDG M&G + transfer to a hotel), call get_quote first for the transfer leg and pass the resulting quote_id + vehicle_id in `transfer`. If lead time is below the minimum (12h before pickup), returns LEAD_TIME_TOO_SHORT — the caller must fall back to manual notification instead of retrying. lead_time_override=true bypasses the 12h minimum ONLY after Sylvain has explicitly confirmed greeter availability for this exact request (WhatsApp admin approval 'mg ok'); never set it on your own initiative. A 90-minute floor always applies. If flight cannot be validated, returns FLIGHT_NOT_FOUND — ask the customer to re-confirm the flight number, or set flight_unverified=true with manual_airline + manual_origin to force-accept.

  ConnectorNo auth

• wsdot_get_ferry_terminalsA

  wsdot-mcp-serverA

  Returns all WSF ferry terminals with their numeric IDs, names, abbreviations, and coordinates. Call this first to resolve human-readable terminal names (e.g. "Bainbridge Island", "Seattle", "Kingston") to the numeric terminal IDs required by the schedule and space tools. The terminal list is small (20 terminals) and rarely changes.

  ConnectorNo auth

• forensic_windows_analysisA

  cpp-cpm-engineA

  Run forensic windows analysis (AACE RP 29R-03 §3.3, MIP 3.3 Observational / Dynamic / Contemporaneous As-Is) across multiple Primavera P6 XER snapshots and return the full analysis dict. This is the headline forensic tool — it computes per-window completion shifts, per-window slip registers (per-activity slip with a critical flag (total float of zero or less, or the driving path in a schedule with negative float)), per-window duration growth on critical-path activities, per-window per-party attribution (Owner / Contractor / Concurrent / Force Majeure / Unattributed), and cumulative project drift from baseline. The attribution math satisfies the CPP conservation check, per the AACE 29R-03 §3.3.E.13 requirement that the summed per-period net impacts equal the difference between the first schedule update and the last schedule update used in the evaluation (per-party day buckets sum to project drift within ±1 day, no cascade-double- counting). Use this tool for the full multi-window forensic claim. If you already have a windows result and only want the per-window × per-party grid view, call ``concurrent_delay_matrix`` instead. Args: schedules: list of dicts in chronological order. Minimum 2 entries (baseline + at least one update). Each dict must contain ``label`` (str) and EXACTLY ONE of: - ``xer_path`` — server-side filesystem path, OR - ``xer_content`` — full XER text content. Use ``xer_content`` when calling a hosted MCP server from a remote client whose XER lives locally. project_name: optional override; auto-picked from XER if "". baseline_idx: which entry in ``schedules`` is the contract baseline (default 0 = first one). entitlement_milestone: optional task_code (e.g. "Ready for Takeover") — recorded on the result, not used for math. output_dir: optional dir for HTML dashboard / DOCX report. If "", a tempdir is used and dropped after — the dashboard / report paths in the response will point to the temp location (caller responsible for moving them). Returns: { "analysis": full dict from run_windows() with keys: "windows", "cumulative", "baseline_label", "data_dates", "attribution_summary", "mcpm_attribution", ..., "dashboard": path to HTML dashboard (server-side), "report": path to DOCX executive report (server-side), "baseline_stability": {"worst_severity", "has_block", ...}, "data_date_corrections": the record of each update whose data date was moved to its latest actual, and of a baseline with actual dates after its data date ([] on clean files); each is also a data-quality warning "analysis"."criticality_basis": the schedules read by their driving path because they have negative float, and those beside them ([] when no schedule has negative float); the box they write is one data-quality warning } On failure: {"error": "..."} with no schedules processed.

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• concurrent_delay_matrixA

  cpp-cpm-engineA

  Build the per-window x per-party concurrent-delay attribution matrix from a chronological list of XER snapshots. Implements the per-window concurrency view per AACE RP 29R-03 §3.3.I (apportionment) and §4.2 (concurrency). Where ``forensic_windows_analysis`` answers "how many days does each party own across the whole project?", this tool answers "how did each window distribute its shift across the parties?" — useful when defending or attacking concurrency findings on a window-by-window basis. CPP conservation check, per the AACE 29R-03 §3.3.E.13 requirement that the summed per-period net impacts equal the difference between the first schedule update and the last schedule update used in the evaluation: the sum of per-party column totals equals the sum of per-window completion shifts within ±1 day of rounding. The column-total definition is this tool's own bookkeeping, not an AACE rule. The ``conservation_check`` field on the response reflects this; ``conservation_diff_days`` carries the exact gap. IMPORTANT — conservation is NOT attribution. ``conservation_check`` can be True (the columns sum to the grand total) even when 100% of the shift lands in the Unattributed column, i.e. no party owns any of the drift. Read ``unattributed_share_pct`` and ``high_unattributed_share_warning`` to know whether a meaningful apportionment actually occurred. A fully-unattributed matrix conserves perfectly but attributes nothing — never present its green conservation check as a validated apportionment. Use this tool when you only need the matrix view; use ``forensic_windows_analysis`` for the full claim. Args: schedules: chronologically ordered list of dicts — the SAME shape ``forensic_windows_analysis`` accepts. Each dict carries ``label`` (optional) and EXACTLY ONE of ``xer_content`` (full XER text, hosted/remote use) or ``xer_path`` (server-side path, local use). This is the preferred input for hosted/remote clients. xer_paths: legacy chronologically ordered list of server-side XER file paths (local-server use). xer_contents: legacy chronologically ordered list of XER text contents. Each element is the full text of one XER. Supply EXACTLY ONE of schedules / xer_paths / xer_contents (lists must have at least 2 entries either way). Returns: { "parties": ["Owner", "Contractor", "Concurrent", "Force Majeure", "Unattributed"], # Unit for every shift_* field and the grand totals. Always # "working_days" — the matrix measures the completion shift # in working days (Dana default). The *_calendar_days twins # express the SAME shift in calendar days so an unlabeled # "11" can never be mistaken for the 15-calendar-day value. "shift_unit": "working_days", "rows": [{ "window_label", "period_start", "period_end", # shift_days == shift_workdays (working days, # legacy alias). shift_calendar_days is the same # shift in calendar days; shift_basis names the # finish driver the shift was measured on. "shift_days", "shift_unit", "shift_workdays", "shift_calendar_days", "shift_basis", "parties": {party: days}, "cascade_inferred": bool }, ...], "column_totals": {party: days}, "grand_total_shift": int, # working days (legacy) "grand_total_shift_workdays": int, "grand_total_shift_calendar_days": int | None, "conservation_check": bool, "conservation_diff_days": int, # Disambiguates "conserved AND attributed" from "conserved # but entirely Unattributed". unattributed_share_pct is # |Unattributed| / sum|shift| as a percent; the warning # flips True when that share is dominant (>= 50%). "unattributed_share_pct": float, "high_unattributed_share_warning": bool, "standard": "AACE RP 29R-03 §3.3.I (apportionment) · §4.2 (concurrency)", "data_date_corrections": the record of each update whose data date was moved to its latest actual, and of a baseline with actual dates after its data date ([] on clean files); each is also a data-quality warning }

  ConnectorNo auth

• collapsed_as_builtA

  cpp-cpm-engineA

  Collapsed As-Built / But-For analysis on a post-impact XER. Implements AACE RP 29R-03 §3.8 Modeled / Subtractive / Single Base method (paired with MIP 3.3 Windows for the dual-method gap report per SCL §11.5). Validates a forensic windows analysis (MIP 3.3) by independently computing the same project drift via subtractive removal of delays from the as-built schedule. For each delay event, the as-built duration of every ``affected_activity`` is shortened by ``impact_days`` working days on that activity's calendar (or removed entirely if ``removal_method="remove"``), then CPM re-runs and the resulting "but-for" finish date is compared to the as-built finish. Cumulative pass removes ALL events at once for a project-level but-for finish. Every finish shift is stated in working days on ``workday_calendar``; the calendar-day span of the same shift is kept beside it, and ``day_unit_basis`` names the unit of every bare ``*_days`` key in the result. Use this tool when opposing counsel demands a but-for analysis or you need a dual-method validation pairing §3.3 (windows) with §3.8 (collapsed-as-built). For prospective fragnet insertion (MIP 3.7), use ``time_impact_analysis_fragnet`` instead. Args: as_built_xer_path: server-side post-impact XER (after delays incurred). as_built_xer_content: full text of post-impact XER (alternative for hosted/remote use). Supply EXACTLY ONE of path/content. delay_events: list of event dicts. Each must have ``event_id``, ``affected_activities`` (list of task_codes), and ``impact_days`` (number of working days on the affected activity's calendar). Optional: ``removal_method`` ('shorten'|'remove'), ``responsible_party``, ``name``, ``description``. output_dir: optional output dir for HTML/CSV (tempdir if ""). project_name: optional override. removal_method: global default 'shorten' or 'remove'. contractor_filter: when True, exclude contractor-caused events from the cumulative pass (owner audit mode). Returns: { "as_built_finish": "YYYY-MM-DD", "per_event_results": [{event_id, but_for_finish, impact_workdays_collapsed, collapse_state, duration_removal_basis, finish_driver_after_removal, ...}, ...], # impact_workdays_collapsed is the event's finish shift in # working days on workday_calendar. Each event also carries # impact_days_collapsed: the same shift in calendar days. # duration_removal_basis discloses WHAT duration was removed # and on what basis; finish_driver_after_removal discloses # WHAT drives the but-for finish (incl. whether it is bound by # the data-date floor) so a reader sees WHY the finish did or # did not move across data dates. "cumulative_but_for_finish": "YYYY-MM-DD", "cumulative_impact_workdays": int, # working days on workday_calendar "cumulative_impact_days": int, # the same shift in calendar days "cumulative_collapse_state": str, # a 'locked_by_actuals' zero is not "no impact" "workday_calendar": str, # the calendar every working-day figure is stated on "day_unit_basis": dict, # each bare *_days key: 'working_days' or 'calendar_days' "dual_method_gap": dict | None, # working days only "output_files": {...}, "warnings": [...], "method": "AACE 29R-03 §3.8 (Modeled/Subtractive/Single Simulation)" }

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• map_ownershipA

  ARM Verify — Entity Intelligence APIA

  Trace the corporate ownership chain above a company and identify the natural persons who ultimately control it. RETURNS JSON with: ownership_chain (array of parent entities, each with name, relationship, jurisdiction and is_ubo_candidate), natural_persons (array of identified beneficial owners, each with name, nationality and natures_of_control such as "ownership-of-shares-75-to-100-percent"), lei of the subject entity, complexity ("SIMPLE", "COMPLEX" or "UNKNOWN"), data_confidence ("HIGH", "MEDIUM" or "LOW") and notes describing the chain depth found. SOURCES: GLEIF Relationship API for LEI parent-child records, Companies House PSC (People with Significant Control) Register for UK entities, and the OpenCorporates corporate network. USE WHEN beneficial-ownership disclosure is required under AML or KYC rules, or to detect ownership by a sanctioned parent. Called via MCP — usage is logged but not billed. For metered billing use the REST API with an API key.

  ConnectorNo auth

• wsdot_get_terminal_spaceA

  wsdot-mcp-serverA

  Returns real-time drive-up and reservable vehicle space available at WSF terminals for upcoming sailings. Use for "will I make the ferry?" or "how full is the next sailing?" questions. Optionally filter to a specific terminal by ID (use wsdot_get_ferry_terminals for the ID). driveUpSpaceCount is the key field — zero means the drive-up lane is full. Destinations are arrivingTerminalIds, not the itineraryLabel string: a sailing can serve several terminals, and those IDs are what wsdot_get_ferry_schedule accepts. Results are paged by terminal (default 5, max 20): offset/limit select whole terminals and totalCount counts matching terminals, not sailings — every sailing of a returned terminal is included, so page size varies with how many departures each terminal carries.

  ConnectorNo auth

• app-listA

  SupaMarketers DataC

  List the SupaMarketers Apps available to this account (same catalog as the website), with each app's Credits price. Call without slug for a summary list (optionally filter by query); call with slug for one app's full input_schema, which defines the fields app-run expects. Only apps with api_runnable=true can be run via app-run; apps whose forms require file uploads cannot run here.

  ConnectorAPI key

• generate-api-keyA

  Common PaperB

  Generate a Common Paper API key for the current OAuth user. Pass optional api_key.description. Returns attributes.key once — treat it as a secret; do not echo it to the user unless they ask. Persist the key in client/session storage and reuse it for future HTTP calls; only call this tool again if a request fails with 401/403 or the stored key is missing. Use the key as Authorization: Bearer for direct HTTP calls (e.g. uploading attachments to POST /v1/attachments).

  Connector

  Destructive

  OAuth

• generate-swagger-apiA

  docuwriterA

  Use this when the user supplies API source code and asks for an OpenAPI or Swagger specification. After authorization, this state-changing action consumes AI credits, creates a generation, and returns OpenAPI JSON; it accepts one source_code file or multiple files entries and can save the result to a Space, where it may be publicly accessible.

  ConnectorOAuth

• list_templatesA

  HTML/CSS to Image APIA

  Lists the connected organization's templates with their latest versions, newest first. Use this to find a template by name and inspect its content and rendering settings. Results are in data; pass an entry's id as template_id to create_templated_image. For more results, pass pagination.next_page_start as max_version. Stop when data is empty or the cursor is null.

  ConnectorOAuth

• update_og_configA

  HTML/CSS to Image APIA

  Replaces an existing Open Graph image configuration and returns the saved configuration in item. Call get_og_config first to inspect settings that should be preserved; omitted optional settings are cleared or reset to defaults. Supply the complete configuration for the chosen config_type. Changes affect subsequent image requests using this configuration. After a plan downgrade, only disabling is accepted and other changes are ignored.

  Connector

  Destructive

  OAuth

• scan_propsA

  flash-props-apiA

  Flatten every active player prop across all of today's games for a sport into a single list. Read-only. No side effects. Requires an API key; rate-limited and row-capped per your tier (Free 15 rows, Builder 100, Pro 3,000, Enterprise 5,000). Returns: { sport, stat, count, rows: Array<{ player, stat, line, overOdds, underOdds, bookCount, gameState?, flashProjection?, eventId, sport, homeTeam, awayTeam, startTime, source, fetchedAt }>, snapshotId, snapshotConsistency }. Each row is a player prop merged with its event context; use homeTeam/awayTeam for matchup context. overOdds/underOdds are American-format integers; null when odds unavailable. Use scan_props when you need a broad cross-game market view. Returns count=0 with an empty rows array (not an error) when no props are posted for the day yet. When not to use: use get_game_props when you already have an eventId; use find_player_props for one player.

  ConnectorNo auth

• get_itinerary_detailsA

  World Airfares Flight SearchA

  Get detailed information about a specific flight itinerary. Returns comprehensive details including: - Complete flight segments with airline, flight number, times, and aircraft type - Departure and arrival terminals (if available) - Baggage allowance (checked bags, carry-on, personal item limits) - Layover information (duration, airport, overnight status) - Fare rules (refundability, change policy, fees) - Price breakdown (base fare, taxes, fees) - Booking URL for purchase Use this when user asks for: - 'Tell me more about this flight' - 'What are the baggage allowances?' - 'Is this flight refundable?' - 'Show me the layover details' - 'What terminals will I use?' WIDGET NOTE: If this tool's results are displayed in a visual widget alongside your response, do NOT repeat the itinerary details in text format. Instead, briefly confirm the details are shown. Do NOT prompt users with instructions like 'say show details for RefNo X' — the widget already displays the details interactively. Only use the formatting instructions below when NO visual widget is rendering the results. PRESENTATION FORMATTING (MANDATORY): - Present detailed information in well-formatted sections. Group by flight leg, segment, and then rules/baggage. - CRITICAL: ALWAYS display full names with codes for better readability: * Airports: Use format 'City Name (CODE)' - Example: 'Colombo (CMB)', 'London Heathrow (LHR)', 'Dubai (DXB)' * Airlines: Use format 'Airline Name (CODE)' - Example: 'Emirates (EK)', 'British Airways (BA)', 'Flydubai (FZ)' * NEVER display codes alone (CMB, DXB, EK, etc.) - users don't recognize them * ALWAYS include both the full name and code in parentheses

  ConnectorNo auth

• resolve_flightA

  bookingA

  Resolve a flight number to its airports, terminals, scheduled times, and status. Codeshares are resolved automatically (e.g. DL8742 → operated by Air France as AF9): the response then carries codeshare:true, marketing_flight and operating_flight — record/track the OPERATING flight. Use BEFORE book_ride whenever the customer provides a flight number so that pickup time and terminal are correct. Input is tolerant: accepts 'AF007', 'AF 007', 'AF-007', 'af7', 'AFR007' — the tool normalizes internally. Returns an array of matching operations (usually 1 when direction is set).

  ConnectorNo auth

• get_snapshotA

  Sugra APIA

  Composed current view of an entity via a named recipe. Executes a fixed server-side recipe (company_snapshot, etf_snapshot, quote_snapshot, macro_indicator_snapshot, macro_calendar, earnings_snapshot, debt_snapshot) and returns one envelope with freshness, provenance, per-component coverage, and billing. Composed calls charge the recipe's fixed cost (1-2 units) from the daily quota. status "partial" means an optional component was unavailable - the present components are still trustworthy; honor the freshness block (stale=true means the data aged past its budget); for macro_calendar, entity is accepted but does not filter the calendar. Args: recipe: Recipe name from the fixed manifest. entity: Entity dict from resolve_entity ({"namespace": ..., "ids": ...}).

  ConnectorNo auth

• Get-Upload-API-InfoA

  YouCam for CreatorsC

  Returns the upload API endpoint and example request/response for uploading images (JPEG, PNG) or videos (MP4) to YouCam AI./n Use when the user wants to upload an image/video via the API. If upload cannot be completed for any reason (including network restrictions, host_not_allowed, or other errors), do not retry. Immediately use the File Upload Widget instead. Do not ask the user for a public URL.

  ConnectorNo auth

• Get-Upload-API-InfoA

  YouCam for Beauty & Personal CareC

  Returns the upload API endpoint and example request/response for uploading images (JPEG, PNG) or videos (MP4) to YouCam AI./n Use when the user wants to upload an image/video via the API. If upload cannot be completed for any reason (including network restrictions, host_not_allowed, or other errors), do not retry. Immediately use the File Upload Widget instead. Do not ask the user for a public URL.

  ConnectorNo auth

• Get-Upload-API-InfoA

  YouCam for Fashion & RetailB

  Returns the upload API endpoint and example request/response for uploading images (JPEG, PNG) or videos (MP4) to YouCam AI./n Use when the user wants to upload an image/video via the API. If upload cannot be completed for any reason (including network restrictions, host_not_allowed, or other errors), do not retry. Immediately use the File Upload Widget instead. Do not ask the user for a public URL.

  ConnectorNo auth

• slip_velocityA

  cpp-cpm-engineA

  Per-window slip velocity & acceleration trend across XER snapshots. Computes three signed metrics per window from the underlying forensic windows analysis: - slip_velocity_days_per_day: completion shift / window duration (positive = slipping, negative = recovering). Numerator is the WORKING-day completion shift. The denominator is WORKING days between the prior and later data dates on the same calendar (``window_duration_workdays``), making this a same-day-type working-day/working-day rate. It falls back to CALENDAR days only for legacy window dicts that predate that field, and such a row is flagged ``velocity_basis="wd/cd"``. Read ``velocity_basis`` to know which denominator produced the figure. Each velocity field name states the ratio it holds: ``slip_velocity_workdays_per_workday`` (populated only on the wd/wd path), ``slip_velocity_workdays_per_calendar_day`` (working-days of slip per CALENDAR day elapsed, computed against ``window_duration_days``), and ``slip_velocity_days_per_day`` as the retained back-compat name for whichever basis was selected. Quote ``basis`` in any expert report. NOTE (2026-09-03): the two named fields are no longer equal. ``slip_velocity_workdays_per_calendar_day`` used to be a blind copy of the headline velocity, which made its name wrong once the denominator moved to working days — it read 5/10 = 0.500 while its name promised 5/14 = 0.357. It now holds the calendar-day rate it is named for. - slip_acceleration: velocity[n] - velocity[n-1] (positive = slip rate increasing, negative = decelerating/recovery) - half_period_estimated_slip_days: shift / 2 (forensic "where were we at the midpoint" centroid estimate), in WORKING days Cumulative aggregates ``mean_velocity_days_per_day`` plus a mean per basis — ``mean_velocity_workdays_per_workday`` and ``mean_velocity_workdays_per_calendar_day`` — each computed only from the rows that actually carry that denominator, so a mean is never labelled with a basis it did not use (None when no window carried it). ``velocity_basis_set`` lists the bases present and ``velocity_units`` describes them, including an explicit MIXED string when a run spans both. Also ``max_velocity_window`` and accelerating / decelerating / recovery window counts. Honest caveats embedded in the response (mandatory for expert reports): midpoint estimates are probabilistic centroids, not observed events; velocity is per-window average, not instantaneous; acceleration is a finite difference, not a true second derivative. Built on top of AACE RP 29R-03 §3.3 windows analysis. Use this tool when you want a slip-rate trend line on top of the same per-window math ``forensic_windows_analysis`` already computes. Args: schedules: chronologically ordered list of dicts — the SAME shape ``forensic_windows_analysis`` accepts. Each dict carries ``label`` (optional) and EXACTLY ONE of ``xer_content`` or ``xer_path``. Preferred input for hosted/remote clients. xer_paths: legacy chronologically ordered list of server-side XER paths. xer_contents: legacy chronologically ordered list of XER text contents (alternative for hosted/remote use). Supply EXACTLY ONE of schedules / xer_paths / xer_contents (at least 2 entries). Returns: { "rows": [{window_label, period_start, period_end, window_duration_days, shift_days, shift_workdays, shift_calendar_days, velocity_basis, slip_velocity_days_per_day, slip_velocity_workdays_per_workday, slip_velocity_workdays_per_calendar_day, velocity_field, velocity_units, slip_acceleration, acceleration_units, midpoint_estimate_date, half_period_estimated_slip_days, half_period_estimated_slip_workdays, half_period_units}, ...], "cumulative": {mean_velocity_days_per_day, mean_velocity_workdays_per_workday, mean_velocity_workdays_per_calendar_day, velocity_basis_set, velocity_units, max_velocity_window, accelerating_windows, decelerating_windows, recovery_windows}, "units": "working-days of slip per working-day elapsed" " (wd/cd fallback wording on legacy windows; " MIXED when a run spans both)", "basis": "<numerator/denominator day-type disclosure>", "standard": "AACE RP 29R-03 §3.3 (Windows Analysis)", "caveat": "...", "data_date_corrections": the record of each update whose data date was moved to its latest actual, and of a baseline with actual dates after its data date ([] on clean files); each is also a data-quality warning }

  ConnectorNo auth

• grant-api-30-day-passA

  Genuine Good GrantsA

  Activate 30 days of bearer-token access to every Genuine Good Grants API tool, subject to a 1,000-request fair-use limit. The client supplies its own random token; Genuine Good stores only its SHA-256 hash. Paid HTTP endpoint: https://genuinegood.online/v1/grants/pass. Price: $15.00 USDC via x402 on Base.

  ConnectorNo auth