MCP
Model Context Protocol (MCP) standardizes how AI apps expose tools, data sources, and capabilities via a uniform interface.
The Model Context Protocol defines a common way for AI applications to register tools, data resources, and UI surfaces that models can use. It decouples tool wiring from prompt hacks.
Teams use MCP to let agents discover and call functions, browse resources, and render UI elements consistently across providers. It simplifies integrating new capabilities without custom glue each time.
In workflows, MCP acts as the contract between models and the host app, improving portability, governance, and observability of tool calls.
Frequently Asked Questions
Why use MCP?
It standardizes tool/resource exposure so agents can be portable across clients and providers. Less custom wiring per tool.
What can MCP expose?
Tools (functions), resources (data/content), and UI surfaces. It describes schemas so models know how to call and present them.
How does MCP improve safety?
By making tool capabilities explicit and typed. You can audit, allow/deny, and log calls with consistent governance.
Does MCP replace RAG or APIs?
No—it standardizes access to them. MCP can wrap APIs and retrieval so the model calls them via a common protocol.
How do I integrate a new tool via MCP?
Define the tool schema, register it with the MCP server, and implement the handler. The model client discovers and invokes it through MCP.
Can MCP work with multiple models?
Yes—the protocol is model-agnostic. Any client that speaks MCP can use the exposed tools/resources.
How do I monitor MCP calls?
Log tool/resource invocations with inputs, outputs, and timing. Add tracing to tie calls back to user/model sessions.
What about permissions?
Scope tool/resource access per user or role. Enforce auth/ACLs at the MCP server to prevent overreach.
Is MCP production-ready?
It’s emerging—great for standardizing internal tools and experimenting with portable agents. Harden with logging, auth, and SLAs.
Agentic AI
An AI approach where models autonomously plan next steps, choose tools, and iterate toward an objective within guardrails.
Agentic Workflow
A sequence where an AI agent plans, executes tool calls, evaluates results, and loops until success criteria are met.
Agent Handoff
A pattern where one AI agent passes context and state to another specialized agent to keep multi-step automation modular.
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