If you are building an AI agent, the hardest part is rarely the model. It is getting the model trustworthy, current data at the moment it needs to act. A language model trained months ago has no idea what Polymarket is pricing a question at right now. The Model Context Protocol (MCP) is how you close that gap: it lets an agent call external tools mid-reasoning. Here is how that works for prediction-market data, and why it matters.
What an MCP server actually is
An MCP server exposes a set of tools, each with a name, a description, and typed inputs and outputs. An MCP-aware client (Claude, Cursor, an n8n flow, a custom agent) reads that list and can decide, on its own, to call a tool when it needs something it does not know. The agent sends the inputs, your server runs, and the result comes back into the model's context as a fact it can reason over.
So instead of hardcoding an API call into your app, you publish a tool like get_market_odds(question) and any agent that connects can use it. The agent figures out when to call it.
Why prediction markets are a good fit
