What Is an MCP Server?
MCP stands for Model Context Protocol. It's an open standard that gives AI assistants a structured way to call external tools and access outside data — without custom integrations for every model.
One protocol, any model, any tool
Without MCP, every AI integration is custom. You'd need to build separate connectors for Claude, ChatGPT, Copilot — and rebuild when things change. MCP eliminates that by defining a universal interface.
Build a tool once as an MCP server, and it works with every compatible AI assistant. Switch models, add new ones, upgrade — the tools keep working. No vendor lock-in, no model-specific wiring.
How does it work?
The AI discovers available tools
When you connect an MCP server, your assistant sees a list of tools it can call — like "find the candidate tariff headings for this product" or "look up the rulings on it."
The AI calls the right tool at the right time
When you ask a question that needs external data, the AI invokes the tool through MCP — sending a structured request and getting back structured results.
You get a grounded answer
Instead of guessing from training data, your assistant reasons over real reference data — tariff schedules, regulatory rulings, live databases — and can cite what it relied on. The tool supplies the evidence; the model still does the thinking.
Why should you care?
Open standard
MCP is not owned by any one AI company. It works across models and platforms, so your investment in tools carries forward.
Your data stays put
MCP servers run in your environment. Your documents and queries don't get sent to a third-party service you didn't choose.
Better than prompting
Prompt engineering is fragile. MCP gives the AI structured tools with real data — repeatable, testable, and auditable.
Future-proof
When a better model ships, your MCP tools work with it automatically. You upgrade the AI without rebuilding integrations.
Where does Proofwork fit?
Anyone can build an MCP server. The hard part is knowing whether what it hands your assistant is any good. Proofwork builds MCP servers for high-stakes domains — trade compliance, regulatory classification — and the work is the curation: which sources are in, how far each row was checked, whether the code it cites still exists, and what the tool says when nothing matches.
Same open protocol. What is different is that each skill publishes its accuracy with the scope that makes it true, the method behind it, and the ways it is known to fail — including the claim we withdrew and why. Proofwork is never in the inference path: we never call a model and never see your conversation. Read the methodology.
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