---
title: "What You’re Learning: OpenAI Agents SDK + MCP Integration"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# What You’re Learning: OpenAI Agents SDK + MCP Integration

This section bridges the gap between the open, interoperable world of MCP and a popular, production-grade agent framework. It shows how MCP is not a competing standard, but a powerful **complement** that makes agentic frameworks more modular, testable, and extensible.

## **Big Picture**
You’re exploring how to connect OpenAI Agents (using the OpenAI Agents SDK) to external context via the Model Context Protocol (MCP), specifically using the stateless, scalable `streamable-http` transport. This enables agents to discover and invoke tools and prompts dynamically, making your agentic systems more modular, interoperable, and production-ready—fully aligned with the DACA design pattern.

## Why This Architecture is Powerful (DACA-Aligned)

-   **Decoupling:** The agent's core logic (the OpenAI Assistant) is completely decoupled from the tool's implementation (the MCP Server).
-   **Interoperability:** The same MCP server that we built and tested throughout this course can be used by an OpenAI agent, a LangChain agent, or any other framework, without changing a single line of server code.
-   **Scalability & Maintainability:** You can develop, test, and scale your tools (the MCP server) independently from your agent. Different teams can own different tool servers.
-   **Open Core:** This aligns with the DACA principle of using open standards (MCP) to connect to managed services or closed frameworks (OpenAI API) at the edges.

---

### **Module-by-Module Breakdown**

#### **01_agent_mcp_http**
- **Goal:** Connect an OpenAI agent to a single MCP server using the `streamable-http` transport.
- **Skills:** 
  - Configure agent to use MCP server as a tool source.
  - Understand the basic agent-to-MCP interaction loop.

#### **02_caching_tool_lists**
- **Goal:** Optimize performance by caching the list of tools from the MCP server.
- **Skills:** 
  - Enable/disable tool list caching.
  - Understand trade-offs between fresh tool discovery and latency.

#### **03_static_tool_filter**
- **Goal:** Filter available tools using static allow/block lists.
- **Skills:** 
  - Restrict which tools are visible to the agent.
  - Use static configuration for tool access control.

#### **04_dynamic_tool_filters**
- **Goal:** Filter tools dynamically based on agent context or runtime conditions.
- **Skills:** 
  - Implement callable filters for tool selection.
  - Make tool availability context-aware (e.g., user, session, environment).

#### **05_prompt_server**
- **Goal:** Serve prompts and tool definitions from a dedicated MCP server.
- **Skills:** 
  - Separate prompt/tool logic from agent logic.
  - Use MCP to serve dynamic or static prompts to agents.

#### **06_agent_with_multiple_mcp_servers**
- **Goal:** Connect a single agent to multiple MCP servers at once.
- **Skills:** 
  - Aggregate tools from several sources.
  - Enable agents to orchestrate across distributed tool servers.

#### **shared_mcp_server**
- **Goal:** Provide a reusable, shared MCP server implementation.
- **Skills:** 
  - Understand the server-side of MCP.
  - Reuse and extend a common MCP server for different tool sets.

---

### **Key Learning Outcomes**
- **Decoupling:** Agent logic is separated from tool implementation.
- **Interoperability:** Any agent framework (OpenAI, LangChain, etc.) can use the same MCP server.
- **Scalability:** Tool servers and agents can be developed, deployed, and scaled independently.
- **Extensibility:** Easily add, remove, or update tools without changing agent code.
- **DACA Alignment:** Follows the DACA pattern of open core, managed edges, and modular, cloud-native design.

---

**In summary:**  

You’re mastering how to build agentic systems where agents can flexibly discover and use tools from one or more MCP servers, with advanced filtering and caching, all in a way that’s scalable, maintainable, and ready for real-world production.

By the end of this module, you will have a practical understanding of how to extend your OpenAI Agents with powerful tools and resources made available through the Model Context Protocol.
