The Model Context Protocol is a framework that lets AI systems interact with tools outside the model—such as databases, APIs, software platforms, or custom applications. It provides a structured way for developers to give an AI model additional capabilities without exposing sensitive data or relying on ad-hoc integrations.
MCP works by defining “tools” the AI can use, along with clear rules about what each tool does and what information it can access. This gives AI models controlled access to external functions, such as pulling market data, writing files, querying databases, or running custom workflows. Because the protocol standardizes how tools communicate, developers can build integrations that work across different AI models and environments.
The goal of MCP is to make AI systems more useful and more secure. Instead of hard-coded or opaque connections, it offers transparency, strict permissions, and predictable behavior. This helps organizations safely connect AI to real business data, internal systems, or specialized services.
MCP gives AI models safe, consistent access to real tools and data. It improves reliability, reduces integration work, and creates a clear boundary between the model and external systems.
MCP creates a standardized, permission-based structure for connecting AI to tools. Developers don’t need custom glue code for each model or service. Because everything flows through clear interfaces, integrations are easier to maintain, easier to audit, and less risky. This also makes AI workflows more predictable and repeatable.
MCP tools operate with explicit permissions, meaning the AI only accesses what developers allow. Tools cannot be used outside their defined scope, and all interactions are traceable. This reduces the risk of unintended file access, unauthorized API calls, or data leaks—problems that can arise with less structured integrations.
MCP is used to connect AI to databases, APIs, file systems, research tools, code execution environments, CRM systems, data warehouses, or any internal application. It enables tasks such as retrieving live data, updating documents, generating reports, analyzing datasets, or triggering workflows, all while maintaining strong control over what the AI can and cannot do.
A financial research team connects an AI assistant to their internal data warehouse using MCP. The AI can securely query approved datasets, generate reports, and analyze metrics—without gaining access to private folders or unauthorized systems.
All FinFeedAPI’s APIs— Stock API, Currencies API, SEC API, Flat Files S3 API and Prediction Markets API —can be connected to an MCP tool, allowing AI systems to fetch live or historical financial data securely and integrate it into models, dashboards, or automated research workflows.
