Discover how MCP is changing the way AI systems like Claude interact with external services and data sources.
Model Context Protocol (MCP) is a groundbreaking communication framework that serves as a bridge between AI assistants like Claude and external services, tools, and data sources. Think of it as a universal translator that eliminates the need for writing tedious integration code while providing AI systems with rich context and powerful capabilities.
Traditional Approach: Building custom integrations for every service your AI needs to access
MCP Approach: Connect once to specialized MCP servers that handle all the complexity
Imagine you're building a chat interface where users can ask Claude about their GitHub data. A user might ask "What open pull requests are there across all my repositories?" Without MCP, you'd need to:
MCP shifts this burden by moving tool definitions and execution from your server to dedicated MCP servers.
Your application server that communicates with Claude
Specialized interfaces to external services
Tools are functions that Claude can call to perform actions. With the Python SDK, tool creation becomes incredibly simple:
@mcp.tool(
name="read_doc_contents",
description="Read the contents of a document and return it as a string."
)
doc_id: str = Field(description="Id of the document to read")
):
if doc_id not in docs:
raise ValueError(f"Doc with id {doc_id} not found")
return docs[doc_id]
Resources provide read-only access to data, similar to GET endpoints in REST APIs:
@mcp.resource(
"docs://documents/{doc_id}",
mime_type="text/plain"
)
if doc_id not in docs:
raise ValueError(f"Doc with id {doc_id} not found")
return docs[doc_id]
Pre-built, high-quality instructions that give better results than user-written prompts:
@mcp.prompt(
name="format",
description="Rewrites the contents of the document in Markdown format."
)
body {
doc_id: str = Field(description="Id of the document to format")
) -> list[base.Message]:
prompt = f"""
Your goal is to reformat a document to be written with markdown syntax.
The id of the document you need to reformat is:
<document_id>
{doc_id}
</document_id>
"""
return [base.UserMessage(prompt)]
Sampling allows MCP servers to access language models through connected clients, shifting the cost and complexity away from the server:
@mcp.tool()
async def summarize(text_to_summarize: str, ctx: Context):
prompt = f"Please summarize the following text: {text_to_summarize}"
result = await ctx.session.create_message(
messages=[SamplingMessage(role="user", content=TextContent(type="text", text=prompt))],
max_tokens=4000,
)
return result.content.text
Roots solve the file discovery problem by telling MCP servers what directories they can access:
Logging and progress notifications keep users informed during long operations:
async def research(topic: str, *, context: Context):
await context.info("About to do research...")
await context.report_progress(20, 100)
# ... research logic
await context.info("Writing report...")
await context.report_progress(70, 100)
MCP uses JSON messages for all communication:
Use Case: Simplest option for same-machine communication
Client and server communicate through stdin/stdout streams
Use Case: Remote servers with full MCP functionality
Uses Server-Sent Events (SSE) to enable server-initiated communication
Every MCP connection follows a strict handshake sequence:
Let's trace a complete user query through the system:
# Enable for horizontal scaling with load balancers
stateless_http=True
Trade-offs: No server-initiated requests, sampling, or progress notifications
# Disable streaming, get plain JSON results
json_response=True
Use Case: Systems that expect traditional HTTP responses
No more writing complex integration code for every service
Consistent patterns for tools, resources, and prompts
Sampling shifts AI costs to clients, not your server
Roots provide controlled file system access
Begin development locally with the simplest transport method before moving to HTTP for production.
Explore the growing ecosystem of pre-built MCP servers for services like:
Create MCP servers for your organization's specific needs and internal services.
Model Context Protocol represents a fundamental shift in how AI systems interact with the world. As the ecosystem grows, we can expect to see:
MCP isn't just another protocolβit's a paradigm shift that makes AI integration accessible, maintainable, and scalable. By abstracting away the complexity of tool definitions and external service integrations, MCP allows developers to focus on building amazing AI-powered experiences rather than wrestling with API integration code.
Whether you're building a simple chatbot or a complex AI assistant, MCP provides the foundation for creating robust, scalable, and maintainable AI applications that can leverage the full power of external services and data sources.