Model Context Protocol

Model Context Protocol (MCP) is an open standard, not a SaaS product, meaning your only cost is the compute required to run the lightweight connectors. Released by Anthropic in late 2024, it standardizes how AI assistants talk to external systems—replacing the chaos of vendor-specific integrations with a universal "driver" model. If you write an MCP server for your internal Postgres database today, it works instantly with Claude Desktop, Cursor, Zed, and any other MCP-compliant client.

For a team integrating three internal tools (e.g., a database, a ticket system, and a log store), the alternative is often building custom glue code for every AI platform you use. With MCP, you build the connector once. In terms of cost, running three Python-based MCP servers on a single AWS t3.small instance costs roughly $15/month. Compare that to a managed automation platform like Zapier or a heavy-duty agent framework that might charge per-run fees. The real savings, however, aren't in hosting—they're in engineering hours. You stop maintaining separate integration logic for LangChain, OpenAI, and LlamaIndex.

Think of MCP like USB-C for AI. Before USB-C, we had a drawer full of proprietary cables for every device. MCP forces AI tools to agree on a single shape for the plug, so data flows freely between your local files and your editor without complex adapters.

The protocol excels at local and private deployments. Because MCP servers can run locally on your machine (communicating via standard input/output) or privately within your VPC (via Server-Sent Events), you don't need to expose internal APIs to the public internet via webhooks—a massive security upgrade over OpenAI Actions. The official SDKs for Python, TypeScript, and Java are robust, and the ecosystem of pre-built servers (for Google Drive, Slack, GitHub) is growing fast.

However, MCP introduces deployment complexity. Unlike a simple REST API call, an MCP server is a persistent process that must be managed, monitored, and secured. If you’re just building a simple script to summarize a PDF, setting up a client-host-server architecture is overengineering. Additionally, while the standard is stabilizing, some edge cases around authentication and remote connections are still rougher than mature REST implementations.

Use MCP if you are building internal tools that need to work across multiple AI interfaces (e.g., IDEs and chat apps) or if you require strict data privacy without exposing webhooks. Stick to simple function calling if you're building a single-purpose bot with no need for interoperability.

Pricing

Since MCP is an open standard (MIT License), there is no 'free tier' or 'enterprise plan'—it is entirely free to use. The actual cost is the infrastructure to host the MCP servers. For local development (e.g., connecting a local SQLite DB to Claude Desktop), the cost is $0. For enterprise deployment, a set of 5-10 low-traffic MCP servers can easily run on a $5/month VPS or a spare container in your existing K8s cluster. The 'hidden cost' is maintenance: you are responsible for keeping these server processes running, secure, and updated, unlike managed integrations in paid platforms.

Technical Verdict

The SDKs (Python, TS, Java) are clean, strictly typed, and handle the JSON-RPC negotiation invisibly. You can spin up a compliant server in under 100 lines of code. Latency is negligible for local (stdio) connections but depends on network conditions for remote (SSE) setups. The documentation is excellent for the basics but gets sparse on advanced topics like complex authentication flows or multi-tenant gateway architectures. It integrates seamlessly with LangChain, but native implementation often feels cleaner.

Quick Start

# pip install mcp
from mcp.server.fastmcp import FastMCP
 
# Create a server named "math-tools"
mcp = FastMCP("math-tools")
 
@mcp.tool()
def add(a: int, b: int) -> int:
    """Add two numbers"""
    return a + b
 
# Run with: mcp run server.py
# Connects via stdio by default

Watch Out

• Stdio transport is default but only works locally; you need SSE (Server-Sent Events) for remote deployment.
• Security is entirely up to you; an MCP server with file access will delete files if the LLM tells it to.
• Debugging JSON-RPC errors over stdio can be painful without good logging middleware.
• Not all clients support all MCP features (e.g., sampling or roots) equally yet.