Use Cases

AI-Powered Exception Management

Trigger: A shipment is flagged as delayed.

The agent:

  1. Calls get_shipment_summary — a single compound tool call that returns a full snapshot of the shipment, its stops, and its carrier assignment (replacing 5 separate API calls)
  2. Checks get_shipment_state_info to confirm which actions are permitted given the current status
  3. Creates a note via create_shipment_note to log the exception
  4. Coordinates next steps with the carrier

Outcome: The issue is surfaced and actioned proactively with zero human touches required.

Building an Internal Logistics Agent

Trigger: An ops team wants a "logistics assistant" in their daily tooling.

Steps:

  1. Get a Shipwell API token from Settings → API Management
  2. Add one config block to Claude Desktop or Cursor (see Quickstart )
  3. The agent auto-discovers all 90+ tools via MCP — no API docs needed, no custom integration work

Outcome: A working logistics agent is live in under an hour.

Safe AI Automation with Dry-Run Validation

Trigger: A team wants AI to execute carrier assignments autonomously but needs confidence before enabling live writes.

Steps:

  1. Enable SHIPWELL_MCP_DRYRUN=true in your MCP server config
  2. The agent runs in full simulation mode
  3. Review the risk reports — every action is rated low , medium , high , or critical
  4. Enable live writes when the team is satisfied with the agent's behavior

Outcome: Full confidence in AI behavior before any real freight data is touched. See Safety & Write Access for more on dry-run mode.

Rate Shopping & Contract Lane Optimization

Trigger: "What's the best rate for this lane?"

The agent calls plan_shipment_options — a compound tool that pulls active contracts and calculates estimated charges in a single call.

Outcome: Instant rate comparison across all contracts with no manual TMS navigation. See Compound Tools for details.

Search and Filter Shipments

Trigger: An operator needs to find specific shipments across a large history without logging into the TMS.

Ask your AI assistant to search using natural language:

  • "Show me all FTL shipments picked up in Texas this week."
  • "Find any shipments with BOL number 12345."
  • "List all in-transit shipments delivering to California."
  • "Pull a CSV export of all delivered shipments from last month."

Outcome: Shipments found in seconds using plain language — no filters to configure, no reports to run.

Track Freight in Real Time

Trigger: An operator needs a live status update on a shipment without digging through the TMS.

Ask your AI assistant for tracking data and location history:

  • "Get the current tracking status for shipment SHP-12345."
  • "Where is my shipment right now? It was picked up yesterday from Dallas."
  • "Show me the full location history for this shipment."

Outcome: Live tracking data surfaced instantly in conversation, without navigating the TMS UI.

Look Up Carrier Information

Trigger: A team needs carrier details — credentials, equipment types, or current assignments — quickly.

Ask your AI assistant to query carrier data:

  • "Find carrier J.B. Hunt and show me their SCAC and MC number."
  • "What carriers do we have relationships with that handle REEFER equipment?"
  • "Who is the assigned carrier for shipment SHP-99999?"

Outcome: Carrier information retrieved on demand without searching through carrier management screens.

Review Invoices and Settlements

Trigger: An AP or finance team needs to audit the invoice pipeline by status, carrier, or amount.

Ask your AI assistant to query invoices:

  • "List all APPROVED invoices from last month."
  • "Show me invoices from carrier XYZ that are still in RECEIVED status."
  • "How many invoices are overdue based on their due date?"
  • "Find all invoices over $5,000 that have not been paid yet."

Outcome: Invoice pipeline visibility in plain language — no manual filtering or report exports required.

Manage Addresses and Dock Schedules

Trigger: An ops team needs facility details, dock availability, or appointment information without switching tools.

Ask your AI assistant to query facility and dock data:

  • "What are the dock hours for our Chicago warehouse?"
  • "Find the address book entry for our Dallas distribution center."
  • "What appointments are scheduled at our Memphis facility tomorrow?"
  • "Check if our shipment is compatible with the dock requirements at facility X."

Outcome: Facility and scheduling information surfaced in conversation, reducing context-switching for ops teams.

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