A new AI-ready data server lets engineers pull real-time, trusted component specs, pricing and inventory directly into chatbots, IDE copilots and enterprise AI tools, cutting search time and streamlining design workflows.
A new AI-ready server is aiming to change how engineers access product data, pulling verified, real-time component information directly into the AI tools they already use. The Model Context Protocol (MCP) Server, newly launched by Microchip Technology, acts as a bridge between large language models and the company’s public product databases turning conversational prompts into structured, trustworthy engineering data.
At its core, the server addresses a growing problem: generative-AI tools often guess or hallucinate technical details when they lack authoritative context. The MCP Server counters this by supplying exact specifications, datasheets, stock levels, pricing and lead times straight from Microchip’s repositories. Engineers can ask a chatbot for a part’s thermal characteristics, or an IDE-embedded AI agent for pinouts or inventory status, and receive responses backed by the same source data used on Microchip’s website.
The key features are:
- Real-time access to verified component data
- Seamless integration with chatbots and IDE copilots
- Instant retrieval of specs, pricing and inventory
- Supports automation and faster design decisions
Built on the MCP streamable HTTP protocol, the server outputs context-rich, JSON-formatted responses optimized for LLM consumption. This ensures compatibility across AI copilots, intelligent chat systems, enterprise automation agents and LLM-powered development environments, effectively making Microchip’s catalog machine-readable and AI-native.
Beyond convenience, the technology targets a larger shift in how design teams work. As embedded development increasingly intersects with AI-assisted code generation, automated part selection and predictive supply-chain planning, the MCP Server positions itself as a foundational data layer. Engineers can streamline research workflows, automate BOM decisions, or validate component choices without switching websites or manually hunting for updated collateral.
The company sees reliable context delivery as a prerequisite for trustworthy AI reasoning particularly in hardware design, where incorrect assumptions can derail schedules or cause downstream failures. The server is available at no cost, and users can immediately interface with it via REST-style endpoints. By making product intelligence accessible through natural language, the platform aims to shorten design cycles and reduce friction between ideation and implementation. The MCP Server is now publicly accessible, giving developers a direct pipeline from AI queries to authoritative component data, an increasingly critical link as the industry leans into AI-driven design automation.
