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What is MCP?The Model Context Protocol and why it matters for AI agents.

The Model Context Protocol (MCP) is an open standard created by Anthropic that defines how AI agents communicate with external tools and APIs. Think of it as the USB-C of AI — a universal connector that lets any AI model use any tool through a standardized interface.

The problem MCP solves

Before MCP, every AI agent needed custom integration code for every API it wanted to use. If you wanted Claude to search your Slack, read your GitHub issues, and query your database, you needed three separate integrations with three different authentication flows and three different data formats.

MCP standardizes this. An MCP server exposes "tools" — functions that an AI agent can call with structured inputs and receive structured outputs. The agent doesn't need to know the implementation details. It just knows: "there's a tool called search_slack that takes a query and returns messages."

How it works

The MCP architecture has three parts:

MCP Client

The AI model or agent that wants to use tools. Claude, GPT, Cursor, VS Code Copilot — any AI that implements the MCP client spec.

MCP Server

A lightweight service that wraps an API and exposes it as MCP tools. One server per API — a Stripe MCP server, a GitHub MCP server, a Slack MCP server.

Transport

The communication layer. MCP supports stdio (for local tools) and HTTP with Server-Sent Events (for remote tools).

// Example: an MCP tool definition { "name": "search_slack", "description": "Search Slack messages across all channels", "inputSchema": { "type": "object", "properties": { "query": { "type": "string", "description": "Search query" }, "limit": { "type": "number", "default": 10 } }, "required": ["query"] } }
Why MCP needs governance

MCP itself is just a protocol — it defines how tools are described and called, but it says nothing about:

Who is allowed to call which tools?

How are API credentials managed and rotated?

What happens when an agent leaks a secret in a tool response?

How do you audit which agents called which APIs, when, and for whom?

How do you detect a tool definition that contains hidden prompt injection?

ImportantWithout a governance layer, every MCP server has direct access to raw API credentials, and every agent has unrestricted access to every tool. In production, this is a security incident waiting to happen.
Where Statio fits

Statio is the governance layer for MCP. It sits between your AI agents and your MCP servers, adding credential vaulting, access policies, audit trails, and threat scanning — everything the MCP protocol leaves out.

Agents never see raw API keys. Every call is logged. Access policies define exactly which agents can use which tools. And the mcp-gen CLI lets you generate MCP servers from any OpenAPI spec in minutes instead of building them from scratch.

# Without Statio: keys scattered everywhere STRIPE_KEY=sk_live_... GITHUB_TOKEN=ghp_... SLACK_TOKEN=xoxb_... OPENAI_KEY=sk-... # With Statio: one token, all tools governed STATIO_TOKEN=eyJ...
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Security-first MCP governance for AI agents. Credential vaulting, access policies, audit trails.Open source on GitHub
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