Everyone is talking about agents, and right after that, they’re talking about agent-to-agent communications. Not surprisingly, various nascent, competing protocols are popping up to handle it.

But maybe all we need is MCP — the OG of GenAI communication protocols (it's from way back in 2024!).

Last year, Jason Liu gave the second most watched AIE talk — “Pydantic is all you need”.

This year, I (the creator of Pydantic) am continuing the tradition by arguing that MCP might be all we need for agent-to-agent communications.

What I’ll cover:

- Misusing Common Patterns: MCP was designed for desktop/IDE applications like Claude Code and Cursor. How can we adapt MCP for autonomous agents?
- Many Common Problems: MCP is great, but what can go wrong? How can you work around it? Can the protocol be extended to solve these issues?
- Monitoring Complex Phenomena: How does observability work (and not work) with MCP?
- Multiple Competing Protocols: A quick run-through of other agent communication protocols like A2A and AGNTCY, and probably a few more by June 😴
- Massive Crustaceans Party: What might success look like if everything goes to plan?


---related links---

https://x.com/samuel_colvin
https://www.linkedin.com/in/samuel-colvin/
https://github.com/samuelcolvin
https://pydantic.dev/


Timestamps
00:00:00 - Introduction: Speaker Samuel Colvin introduces himself as the creator of Pydantic.

00:00:42 - Pydantic Ecosystem: Introduction to Pydantic the company, the Pydantic AI agent framework, and the Logfire observability platform.

00:01:18 - Talk Thesis: Explaining the title "MCP is all you need" and the main argument that MCP simplifies agent communication.

00:02:05 - MCP's Focus: Clarifying that the talk focuses on MCP for autonomous agents and custom code, not its original desktop automation use case.

00:02:48 - Tool Calling Primitive: Highlighting that "tool calling" is the most relevant MCP primitive for this context.

00:03:10 - MCP vs. OpenAPI: Listing the advantages MCP has over a simple OpenAPI specification for tool calls.

00:03:21 - Feature 1: Dynamic Tools: Tools can appear and disappear based on server state.

00:03:26 - Feature 2: Streaming Logs: The ability to return log data to the user while a tool is still executing.

00:03:33 - Feature 3: Sampling: A mechanism for a tool (server) to request an LLM call back through the agent (client).

00:04:01 - MCP Architecture Diagram: Visualizing the basic agent-to-tool communication flow.

00:04:43 - Complex Architecture: Discussing scenarios where tools are themselves agents that need LLM access.

00:05:24 - Explaining Sampling: Detailing how sampling solves the problem of every agent needing its own LLM by allowing tools to "piggyback" on the client's LLM access.

00:06:42 - Pydantic AI's Role in Sampling: How the Pydantic AI library supports sampling on both the client and server side.

00:07:10 - Demo Start: Beginning the demonstration of a research agent that uses an MCP tool to query BigQuery.

00:08:23 - Code Walkthrough: Validation: Showing how Pydantic is used for output validation and automatic retries (model_retry).

00:09:00 - Code Walkthrough: Context Logging: Demonstrating the use of mcp_context.log to send progress updates back to the client.

00:10:51 - MCP Server Setup: Showing the code for setting up an MCP server using fast_mcp.

00:11:54 - Design Pattern: Inference Inside the Tool: Explaining the benefit of having the tool perform its own LLM inference to reduce the context burden on the main agent.

00:12:27 - Main Application Code: Reviewing the client-side code that defines the agent and registers the MCP tool.

00:13:16 - Observability with Logfire: Switching to the Logfire UI to trace the execution of the agent's query.

00:14:09 - Observing Sampling in Action: Pointing out the specific span in the trace that shows the tool making an LLM call back through the client via sampling.

00:14:48 - Inspecting the SQL Query: Showing how the observability tool can be used to see the exact SQL query that was generated by the internal agent.

00:15:15 - Conclusion: Final summary of the talk's points.

AI Engineer

Samuel Colvin, creator of Pydantic, presents MCP (Model Context Protocol) as a powerful solution for agent-to-agent communication. He demonstrates how MCP's tool calling capabilities, particularly the sampling feature, allow agents to share LLM access efficiently. The talk includes a practical example of building a research agent that queries BigQuery through an MCP server, showcasing features like dynamic tools, logging, and observability. Colvin argues that MCP simplifies complex agent architectures while providing better context management than alternatives like OpenAPI.

MCP is all you need — Samuel Colvin, Pydantic