AI assistants are increasingly useful on their own — but their real potential comes when they can reach outside the conversation and interact with the tools and data you already use. Model Context Protocol (MCP) is an open standard that defines how that connection works. Understanding it helps you see why AI assistants that support it are meaningfully different from ones that don't.
The problem MCP solves
Before standards like MCP, connecting an AI assistant to an external tool required custom integration work for every single pair. A calendar tool needed one bespoke connector for assistant A, a different one for assistant B, and so on. This made the ecosystem fragmented and expensive to maintain. Every new tool or assistant meant more integration work.
MCP defines a common language. A tool that speaks MCP can connect to any assistant that speaks MCP, without either side needing to know the other's internals. This is the same idea that made the web work: once you agree on a standard protocol (HTTP), any browser can load any website.
What MCP actually defines
MCP specifies how an AI assistant can:
- Discover what tools are available to it — what they're called, what they do, what inputs they accept.
- Call a tool — send a structured request and receive a structured response.
- Read context from external sources — documents, databases, APIs — and bring that information into the conversation.
It does not define what the tools themselves do. A tool that searches the web, queries a database, or reads a calendar looks the same to the MCP layer. The protocol is about the connection, not the capability.
Why it matters for the AI tools you use
If an AI assistant supports MCP, it can connect to a growing library of tools without requiring you to wait for the assistant's developers to build each integration themselves. New MCP-compatible tools appear frequently, and any of them can be added to an MCP-capable assistant.
This changes the economics of the ecosystem. Instead of a few large platforms controlling which integrations exist, a broad network of tool developers can build for the standard and reach every assistant that supports it.
For you as a user, it means the AI assistant you use can be genuinely extensible — connected to the specific data and tools your work depends on — without that being a bespoke, expensive project.
MCP is an open standard
MCP was introduced by Anthropic and published as an open specification. Any developer can implement it, any tool can adopt it, and no single company controls it. This openness is part of the point — the value of a protocol comes from widespread adoption, and widespread adoption requires that it not be proprietary.
The specification and documentation are publicly available. Anthropic's Model Context Protocol documentation is the authoritative reference for developers implementing it.
How askFinz relates to MCP
askFinz supports MCP as part of its protocols layer, which means it can connect to the growing ecosystem of MCP-compatible tools and data sources. For users, this shows up as the ability to extend your AI workspace with the specific tools your work relies on.
The goal is that your AI assistant is not limited to what one team has decided to build — it can reach the tools that matter to you.
Further reading
- The protocols that connect askFinz to external tools and standards: Protocols.
- How agents interact with each other using open standards: Agent-to-agent (A2A), explained.
- Anthropic's Model Context Protocol documentation — the authoritative open specification for developers and implementors.