What Is MCP as a Service — and Why It Matters for AI Agents

The problem with AI agents today

AI agents are powerful — but they're only as useful as the data they can access. By default, a model like Claude or GPT-4 only knows what was in its training data. If you want it to query your database, call your API, or read from your internal tools, you need to connect it to those data sources.

That connection layer is called MCP — the Model Context Protocol, an open standard introduced by Anthropic.

What is MCP?

MCP is a protocol that lets AI agents call external tools and read from external data sources in a structured, safe way. An MCP server exposes a set of tools (functions the agent can call) and resources (data the agent can read). The agent uses these to answer questions, take actions, and interact with the real world.

Think of it as an API — but designed specifically for AI.

The infrastructure problem

Setting up an MCP server yourself means:

• Standing up a server (a container, a Lambda, a VPS)
• Managing uptime, scaling, and authentication
• Wiring up your data sources
• Updating it every time your API changes

For teams that just want to connect their data to an agent, this is a lot of overhead.

Shredly's approach

Shredly is MCP as a Service. You describe your data source — a REST API, a database endpoint, a webhook — and Shredly provisions and hosts the MCP server for you. No containers, no ops, no infrastructure.

You get:

• A hosted MCP endpoint your agents can connect to
• An API key for secure authentication
• Compatibility with Claude Desktop, Claude Code, Cursor, Windsurf, Gemini, and any MCP-compatible client

Getting started

1. Sign in at shredly.io
2. Generate an API key from the dashboard
3. Paste your key and endpoint into your MCP client

Your agent can be talking to your data in under five minutes.