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18_MCP_INTEGRATION

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MCP Integration - Comprehensive Deep Dive

Overview

MCP (Model Context Protocol) Integration in the OpenAI Agents SDK enables agents to use tools provided by MCP servers. Think of MCP as a "universal tool protocol" that allows different systems to expose tools to AI models in a standardized way. This is essential for integrating with external tool providers, sharing tools across applications, and building a tool ecosystem.

Core Concepts

What is MCP?

MCP (Model Context Protocol) is:

  • A protocol for exposing tools to AI models
  • Standardized - consistent interface across providers
  • Language-agnostic - works with any programming language
  • Extensible - supports custom tool types
  • Community-driven - open ecosystem of tool providers
Why MCP Matters
  1. Standardization - Consistent tool interface
basic-ques
core
Revision w/ Whiteboard
CN Basics - 1
CN Basics - 2
DNS
Event loop
programming-language-concepts.md
zero-language-explanation.md
DB
Quick
databases-deep-dive.md
01-introduction.md
02-relational-databases.md
03-database-design.md
04-indexing.md
05-transactions-acid.md
06-nosql-databases.md
07-query-optimization.md
08-replication-ha.md
09-sharding-partitioning.md
10-caching-strategies.md
11-cap-theorem.md
12-connection-pooling.md
13-backup-recovery.md
14-monitoring.md
15-database-selection.md
README.md
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01-Orchestration.md
02-DeepResearch.md
03-Search.md
04-Scraping.md
05-Streaming.md
06-MultiProviderLLM.md
07-MemoryAndContext.md
08-ErrorHandling.md
09-RateLimiting.md
10-TaskQueue.md
11-SecurityAndAuth.md
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metro-bundler-deep-dive.md
OpenAI Agents Python
00_OVERVIEW.md
01_AGENT_SYSTEM.md
02_RUNNER_SYSTEM.md
03_TOOL_SYSTEM.md
04_ITEMS_SYSTEM.md
05_GUARDRAILS.md
06_HANDOFFS.md
07_MEMORY_SESSIONS.md
08_MODEL_PROVIDERS.md
09_SANDBOX_SYSTEM.md
10_TRACING.md
11_RUN_STATE.md
12_CONTEXT.md
13_LIFECYCLE_HOOKS.md
14_CONFIGURATION.md
15_ERROR_HANDLING.md
16_STREAMING.md
17_EXTENSIONS.md
18_MCP_INTEGRATION.md
19_BEST_PRACTICES.md
20_ARCHITECTURE_PATTERNS.md
opencode-study
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sqlalchemy - fastapi
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system_design_for_agentic_apps.md
Agent Loop
  • Integration - Easy integration with external systems
  • Ecosystem - Access to community tools
  • Flexibility - Mix and match tool providers
  • Future-proof - Protocol evolves with community input
  • Interoperability - Works across different AI platforms

    • MCP Architecture

    MCP Components
    Plain text

    Flow:

    1. Agent requests tools
    2. MCP client connects to MCP server
    3. MCP server lists available tools
    4. Tools are exposed to agent
    5. Agent calls tools via MCP client
    6. MCP server executes tools
    7. Results returned to agent

    MCP Server Types

    MCPServerStdio

    Standard I/O based MCP server:

    Python

    When to use:

    • Local MCP servers
    • Command-line based servers
    • Simple deployments
    MCPServerSse

    Server-Sent Events based MCP server:

    Python

    When to use:

    • Remote MCP servers
    • Real-time updates
    • Web-based deployments
    MCPServerStreamableHttp

    Streamable HTTP based MCP server:

    Python

    When to use:

    • HTTP-based MCP servers
    • Streaming tool execution
    • Complex server deployments

    MCP Configuration

    MCPConfig

    Configure MCP behavior:

    Python
    Schema Conversion

    Convert tool schemas to strict mode:

    Python

    Benefits:

    • Better model understanding
    • Reduced errors
    • Improved tool calling
    Error Handling

    Custom error handling for MCP tools:

    Python

    MCP Tool Filtering

    Static Tool Filter

    Filter tools statically:

    Python
    Dynamic Tool Filter

    Filter tools dynamically:

    Python
    Tool Filter Context

    Context available for filtering:

    Python

    MCP Tool Metadata

    MCPToolMetaContext

    Context for tool metadata resolution:

    Python
    MCPToolMetaResolver

    Resolve tool metadata:

    Python

    MCP Approval

    Local Approval

    Require approval for MCP tools:

    Python
    Approval Items

    MCP approval creates items:

    Python

    MCP Manager

    MCPServerManager

    Manage multiple MCP servers:

    Python
    Server Lifecycle

    Manage server lifecycle:

    Python

    MCP and Agents

    Adding MCP to Agents

    Add MCP servers to agents:

    Python
    MCP Tool Resolution

    Tools are resolved at runtime:

    Python
    MCP and Handoffs

    MCP tools work with handoffs:

    Python

    MCP and Tracing

    MCP Tracing

    MCP operations are traced:

    Python
    MCP Span Types

    MCP creates specific spans:

    • mcp_list_tools - Listing available tools
    • mcp_call_tool - Calling a tool
    • mcp_approve - Approval process

    MCP Best Practices

    1. Use Appropriate Server Type

    Choose the right server type:

    Python
    2. Filter Tools

    Filter tools to reduce complexity:

    Python
    3. Handle Errors

    Handle MCP errors gracefully:

    Python
    4. Use Strict Schemas

    Use strict schemas for better results:

    Python
    5. Approve Sensitive Tools

    Require approval for sensitive tools:

    Python

    Common MCP Patterns

    1. Multiple MCP Servers

    Use multiple MCP servers:

    Python
    2. Conditional MCP

    Use MCP conditionally:

    Python
    3. MCP Fallback

    Fallback if MCP unavailable:

    Python
    4. MCP Tool Namespacing

    Namespace MCP tools:

    Python
    5. MCP Caching

    Cache MCP tool listings:

    Python

    MCP and Testing

    Testing MCP Integration

    Test MCP tool calls:

    Python
    Mock MCP Server

    Mock MCP server for testing:

    Python

    MCP and Security

    Secure Connections

    Use secure connections for MCP:

    Python
    Tool Validation

    Validate MCP tools:

    Python

    MCP and Performance

    Connection Pooling

    Pool MCP connections:

    Python
    Tool Caching

    Cache MCP tool results:

    Python

    MCP and Monitoring

    Track MCP Usage

    Track MCP tool usage:

    Python
    Monitor MCP Performance

    Monitor MCP performance:

    Python

    Summary

    MCP Integration enables standardized tool access. Key takeaways:

    1. MCP is a protocol for exposing tools to AI models
    2. MCPServerStdio - Standard I/O based server
    3. MCPServerSse - Server-Sent Events based server
    4. MCPServerStreamableHttp - Streamable HTTP server
    5. MCPConfig - Configure MCP behavior
    6. Schema conversion - Convert to strict JSON schema
    7. Error handling - Custom error handlers
    8. Tool filtering - Static and dynamic filtering
    9. Tool metadata - Resolve tool metadata
    10. Approval - Require approval for tools
    11. MCPServerManager - Manage multiple servers
    12. Agent integration - Add MCP to agents
    13. Tracing - MCP operations are traced
    14. Appropriate server type - choose right type
    15. Filter tools - reduce complexity
    16. Handle errors - graceful error handling
    17. Strict schemas - better model understanding
    18. Approval - for sensitive tools
    19. Multiple servers - use multiple MCP servers
    20. Security - use secure connections

    MCP Integration is essential for accessing a standardized ecosystem of tools.