What is MCP?
Overview
The Model Context Protocol (MCP) is the foundational framework of MindCore. It defines how context-specific logic modules are created, deployed, and executed in a decentralized environment. MCP Modules act as programmable units of logic that can ingest data, process information, and return outputs — all while being composable with other modules or agents.
Why MCP?
Traditional AI workflows often lack structure, reusability, and composability. MCP solves this by introducing a standardized module format that allows developers to break down logic into reusable pieces — similar to APIs or smart contracts — that can be plugged into various workflows, including agents, dashboards, or external applications.
Key Properties
Modular: Each MCP module serves a focused purpose (e.g. fetch data, analyze sentiment, scrape web pages).
Composable: Modules can be chained or combined to perform multi-step operations.
Contextual: Inputs can be dynamic and time-sensitive (e.g. price snapshots, social data, user-defined triggers).
Executable On-Demand: Modules can be invoked by users, agents, or workflows, with transparent execution and result logging.
Example MCP Modules
Data Feed MCP Pulls real-time market data from platforms like TradingView, Glassnode.
Web Scraping MCP Gathers and structures data from specified web pages.
Analytics MCP Connects with APIs like Google Analytics or Twitter to extract insights and user behavior data.
Real-World Use Case
Let’s say you want to build an agent that sends alerts when a token breaks resistance while also checking for a spike in Twitter mentions. You can:
Use a Data Feed MCP to track token price in real-time.
Use an Analytics MCP to pull Twitter volume data.
Combine both in an Agent with predefined logic (e.g. “send alert if price > resistance AND tweet volume > threshold”).
That’s the power of MCP: plug, compose, automate.
Ready to deploy your own MCP Module? Head to the next section.
Last updated