Ask any AI what a P0420 means. It’ll answer confidently. It might be wrong.
Models generate vehicle specs, recalls, and codes from patterns, not sources — so they hallucinate. CarVector’s MCP server gives your agent structured, sourced answers it can cite. Not confidently wrong. Verifiably right.
The hallucination problem is worst in specialized domains.
A model trained on last year’s data doesn’t know about a recall filed last week, and it never knew your exact trim’s engine. The fix isn’t more training — it’s runtime access to structured, current data through tool use. That’s exactly what Model Context Protocol is for.
One config block. Point your agent at CarVector.
MCP is an open standard for agents to call external tools mid-conversation. Add CarVector as an MCP server — published on npm, run with your own key — and your agent queries real data instead of guessing.
{
"mcpServers": {
"carvector": {
"command": "npx",
"args": [
"-y",
"carvector-mcp"
],
"env": {
"CARVECTOR_API_KEY": "cv_your_key"
}
}
}
}Four tools. Sourced answers.
Your agent calls search_vehicles, get_vehicle, get_recalls, and lookup_dtc and gets structured data back. “Is a P0300 serious?” returns High severity, safety-critical. “Is a 2021 F-150 under recall?” returns the real count — because the agent chose the right tool, not a guess.
→ carvector.lookup_dtc("P0300")
{
"severity": "High",
"safety_risk": true,
"category": "Powertrain"
}
→ carvector.get_recalls("2021 Ford F-150")
{
"recall_count": 27
}Every vehicle question routes through your tool.
Once your agent has CarVector, every “what engine does my car have,” every “are there recalls on my truck,” every “what does this code mean” becomes a sourced API call — because the agent picked the right tool for the question. That’s distribution embedded in the workflow.
Read the MCP docsYour agent already gets asked about cars. Give it an answer it can stand behind.
Free tier. No credit card.