Ask a large language model what wheels fit a 2024 Toyota Tacoma, and you will likely get a confident but half-right answer. While the bolt pattern is often correct, the center boreβ€”a critical dimension that changed on this generationβ€”is frequently guessed. To solve this, custom forged wheel manufacturer Ankusi Wheels has released a free Model Context Protocol (MCP) server and REST API that provides precise vehicle fitment data without requiring an API key or authentication.

Dataset Scope and Accuracy

The underlying dataset, version 2026.10.19, covers 10,093 vehicle variants across 174 makes and 56 distinct bolt patterns. It includes essential specifications such as lug count, front and rear center bores, factory wheel sizes, and both "flush" and "max" aftermarket options. The data is licensed under CC BY 4.0 and includes a source page for every row, ensuring transparency and verifiability for developers building automotive tools or AI assistants.

MCP and REST Implementation

The service exposes three read-only tools via a Streamable HTTP MCP server: lookup_wheel_fitment, vehicles_by_bolt_pattern, and list_makes. It is also listed in the official MCP Registry as com.ankusiwheels/wheel-fitment. For traditional integration, developers can hit the REST API directly using standard HTTP GET requests. For example, querying make=Toyota&model=Tacoma&year=2024 returns structured JSON data including the specific bolt pattern (6x139.7) and the correct center bore (95.1mm), eliminating the ambiguity that plagues general-purpose LLMs.

Why Center Bore Matters

Grouping the data by bolt pattern reveals how insufficient the PCD (Pitch Circle Diameter) is on its own. The 5x114.3 pattern, common across thousands of vehicles, has 16 distinct center bore measurements. Generation changes are the classic trap for AI models; the 2024 Tacoma kept the 6x139.7 bolt pattern but switched its hub bore from 106.1mm to 95.1mm. Similarly, the C8 Corvette moved from the C7's 5x120.65 to a metric 5x120. An assistant checking only the bolt pattern would incorrectly claim old wheels fit the new chassis.

Validation Methodology

To maintain high fidelity, Ankusi Wheels only accepts bolt pattern and center bore changes when two independent references agree. In cases where sources conflict, the field is left blank rather than guessed, and every correction is logged in a public changelog. This rigorous validation process ensures that the data served to AI agents and developers is reliable, preventing the propagation of hallucinated specifications into production applications.

Key Takeaways

  • Free access to a 10,000+ variant wheel fitment dataset with no API key required.
  • Supports both MCP for AI agents and REST for standard application integration.
  • Solves the "center bore" hallucination problem by providing precise, validated data.
  • Licensed under CC BY 4.0 with full source attribution for every data point.

The Bottom Line

This is a pragmatic win for the AI agent ecosystem: it replaces hallucinated automotive specs with a validated, open-source data layer that actually works.