Using Market Data with AI Agents via MCP

What is MCP?

MCP is an open protocol for connecting AI applications to tools and data. An MCP server describes a set of tools, each with a name, a plain-language description and a typed list of parameters. An MCP client, such as a desktop AI assistant, an IDE or your own agent, connects to the server, reads that list, and offers the tools to the model.

The model never talks to your database or API directly. It asks the client to call a tool with specific arguments, the client calls the server, and the result goes back into the conversation as context.

How an agent calls a market data tool

Suppose you ask: "Is gold overbought on the hourly chart?" A typical sequence looks like this:

• The model sees tools such as get_price and get_rsi in its tool list, with descriptions of what they return.
• It decides it needs data and requests get_rsi with symbolCode XAUUSD and timeFrame H1.
• The client sends that call to the MCP server, which validates the arguments, fetches the data and returns JSON.
• The model reads the result, maybe calls get_price for the latest quote, and then writes its answer using real numbers instead of guesses.

Connecting to the RealMarketAPI MCP server

RealMarketAPI hosts an MCP server over HTTP at the address below. Access requires a PRO plan or higher. Add it as a remote MCP server in any client that supports the HTTP transport; the exact steps differ by client, so follow your client's documentation for adding a server by URL.

The server exposes tools for live prices and candles (get_price, get_candles), history, symbols and timeframes, indicators such as get_sma, get_ema, get_rsi, get_macd, get_atr and get_bollinger_bands, and analysis tools such as support and resistance, volatility and sentiment. The tools take your API key as an argument, so the agent will need it in order to call them.

https://ai.realmarketapi.com/mcp

What agents are good at here

Agents are useful for the slow parts of analysis: pulling several indicators at once, summarising a multi-timeframe picture, explaining what a reading means, or drafting the code for a backtest you then run yourself.

• "Compare RSI and ATR for XAUUSD and BTCUSD on H4 and summarise the differences."
• "Fetch the last 50 H1 candles for EURUSD and describe the structure."
• "Explain why the MACD histogram turned negative on this chart."

Safety and limits

An AI answer built on real data is still an opinion, not financial advice. Models can misread a number, mix up timeframes, or present a confident conclusion that the data does not support. Treat the output as a research assistant's draft.

• Verify any number that drives a decision against the raw tool result or your chart.
• Do not let an agent place trades without hard limits on size, frequency and loss, plus a human check.
• Use a dedicated API key for agents, keep it out of shared prompts and screenshots, and rotate it if it leaks.
• Be careful with content the agent reads from the web or documents: it can contain instructions meant to steer the model.
• Backtest any strategy an agent suggests before risking money. A plausible explanation is not evidence of an edge.

Frequently asked questions