We built AlphaLoops to give go-to-market teams in transportation a faster path to carrier intelligence. Today, we're making it even faster.
We're announcing the AlphaLoops MCP Server — a remote Model Context Protocol server that connects our carrier data directly to AI tools like Claude, ChatGPT, and any LLM client that supports MCP. No more copy-pasting from dashboards. No more switching tabs. Your AI assistant can now query AlphaLoops in real time, right inside your workflow.
What Is MCP?
Model Context Protocol (MCP) is an open standard that lets AI models securely connect to external data sources and tools. Think of it as a universal plug for AI — instead of building one-off integrations for every tool, MCP gives developers and power users a single standard to wire up their AI to live data.
For sales reps, analysts, and ops teams in freight, that means your AI can now reach into AlphaLoops and pull carrier data, fleet info, and contact intelligence on demand — without you lifting a finger.
What You Can Do With It
The AlphaLoops MCP Server exposes 13 tools covering the full carrier intelligence stack:
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Carrier lookups by USDOT or MC/MX number
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Fuzzy search carriers by company name
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Deep filtered queries with geo radius (e.g., carriers within 10 miles of Austin, TX)
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Operating authority history
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Fleet data — registered trucks and trailers
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Inspection and violation records
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Crash history
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Contact search — find the right people at a carrier
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Contact enrichment — verified emails and phone numbers
See It In Action
In our demo video, we walk through two real-world examples that show what's possible when carrier intelligence meets AI.
Example 1: Building a Personalized Prospect Email
Instead of manually pulling a carrier profile, digging through contacts, and drafting an outreach email by hand, we asked Claude to do it all in one shot. It queried AlphaLoops for the carrier's fleet data and key contacts, then used that information to write a personalized, relevant cold email — tailored to what that specific carrier actually looks like.
This is the kind of workflow that used to take 20 minutes. With MCP, it's a single prompt.
Example 2: Find Carriers Within 10 Miles of Austin, Texas
We asked the AI to surface all carriers operating within a 10-mile radius of Austin. Using the carrier_filtered_query tool with a geo radius filter, it returned a targeted list — the kind of segmentation that would normally require a CRM admin or a custom data pull.
Same data. Faster. Directly in your AI interface.
How to Connect
Add the AlphaLoops MCP server to your client configuration:
json
{
"mcpServers": {
"alpha-freight": {
"type": "url",
"url": "https://mcp-freight.runalphaloops.com/mcp",
"headers": {
"Authorization": "Bearer <your_api_key>"
}
}
}
}Don't have an API key yet? Contact us.
For Claude specifically, you can connect the server directly in your Claude settings under "Integrations." Once connected, Claude can query your carrier data in any conversation.
Why This Matters
The freight industry runs on relationships and data. The problem has never been a lack of carrier data — it's been the time it takes to find the right carrier, find the right contact, and act on it.
MCP changes that by collapsing the distance between your AI and your data. When the model can reach into AlphaLoops directly, you stop spending time on data retrieval and start spending it on decisions.
This is early, and we're building fast. More tools, deeper enrichment, and tighter CRM integrations are coming.
Ready to connect?
Visit mcp-freight.runalphaloops.com to get started, or reach out to us at runalphaloops.com if you need an API key.