My Takeaways from Anthropic's MCP Session

Hey there, fellow developers! I recently watched a YouTube session by Anthropic about the Model Control Protocol (MCP), and I couldn’t resist taking some handwritten notes to capture the key points. I’ve included those sketches below for reference (converted hand sketched diagrams to eraser diagrams), and if you want to check out the session yourself, you can find it here.

MCP is a fascinating topic that ties together AI, agents, and seamless integration with tools and data sources, so let’s dive into what I learned!

What Caught My Attention

The big idea that grabbed me was this: models are only as good as the context provided to them. It’s a simple but powerful reminder that even the most advanced AI needs the right data and tools to deliver useful results.

If the input’s lacking, the output won’t be much better—no matter how smart the model is.

Understanding MCP at a Glance

MCP is described as an open protocol that enables smooth integration between AI agents, tools, and data sources. Here’s what I noted down:

It standardizes how AI applications interact with external systems, covering:

• Prompts

• Tools

• Resources

This standardization feels like a potential game-changer, making it easier to integrate AI across different platforms without reinventing the wheel each time.

The MCP Client and Server Dynamic

I sketched out the relationship between an MCP client and server (you can see this in my first sketch below). The client can:

• Invoke tools

• Query for resources

• Interpolate prompts

Meanwhile, the server:

• Exposes tools

• Exposes resources

• Exposes prompts

This setup creates a collaborative ecosystem where clients and servers work together, giving developers a lot of flexibility in how they build AI-driven solutions.

Key Features of MCP

The session highlighted a few key features of MCP that I found really interesting:

1. An Official MCP Registry API (Coming Soon)

Unified host metadata service: This will enable:

• Discovery

• Centralization

• Verification

I jotted down a possible structure for this (check out my second sketch below):

• registry: npm

• published: ...

• version: 1.2.0

• type: studio

• Another instance with type: sse and a url: ...

Anthropic mentioned that the registry API is still in the works and will be rolled out in the coming days. Once it’s live, it could be a solid foundation for managing and verifying agent interactions, which I’m really looking forward to.

2. Sampling

Sampling allows a server to request completions from a client, giving the user application full control over security, privacy, and cost. I noted down some Sampling parameters:

• Model Preferences & Hints

• System Prompt

• Temperature & Max Tokens

This control is a big deal—it lets us fine-tune how agents behave without being stuck with server-side limitations.

3. Composability

Composability enables agents to chain together while the client application controls inference. My third sketch (see below) shows:

• An application + LLM requesting inference from a client-server orchestrator agent, involving analysis agents and coding agents, all coordinated by a research agent.

This opens up some creative possibilities for designing AI systems that can handle complex workflows.

In this sketch, sampling & composabilty are working together:

Building Effective Agents with MCP

The session also covered how to build effective agents using MCP. Here’s what I took away:

• Tool-Mode Controlled Functions: These are inspired by the model and include:

1. Retrieve/Search

2. Send a message

3. Update DB records

• Resources: Application-controlled data exposure, files, and API responses.

• Prompts: User-controlled, pre-defined templates for AI interactions, with output as JSON.

Simplifying Interactions

MCP simplifies how clients and servers interact by allowing a server to request completions from a client, putting the user application in charge of security, privacy, and cost. It’s a practical way to streamline AI workflows while keeping control in the developer’s hands.

A Practical Example from the Session: Grafana Servers

One example from the video that I found really interesting was about installing and invoking Grafana servers.

The idea was to use MCP to query logs and suggest fixes based on agent insights. My fourth sketch (see below) captures this concept, showing an agent searching for an official Grafana server via the upcoming Registry API.

This use case got me thinking about the automation possibilities once the API launches—being able to seamlessly integrate with tools like Grafana could be a huge time-saver.

Wrapping Up

This session gave me a lot to think about regarding how MCP could transform the way we integrate AI agents with our tools and data. The focus on composability, the upcoming registry API, and the flexibility it offers are really inspiring.

The Grafana example was a great illustration of how MCP can be applied in real-world scenarios, and I’m eager to see the registry API launch soon so I can start experimenting with these concepts in my own projects. If you’ve watched the session or have thoughts on MCP, I’d love to hear your take in the comments!

PS: I have already used the github MCP from Smithery.ai and the browsermcptools from agentdeskai in my cursor for 2 projects.

Until next time, happy coding!🚀