What MCP (Model Context Protocol) Actually Solves — and What It Doesn't
Key Takeaways
- —MCP standardizes how AI applications connect to external data sources and tools, replacing custom point-to-point API integration code.
- —The protocol handles client-server negotiation, resource capabilities, and tool schemas via standard JSON-RPC 2.0 transport.
- —MCP solves connection standardized interfaces; it does not solve security authentication policies or tool execution authorization by itself.
Model Context Protocol (MCP) has emerged as an open standard for connecting AI models to secure local and remote data sources. Understanding its architecture clarifies where it simplifies system design and where application-level security remains necessary.
The N×M integration problem in AI engineering
Prior to standardized protocols, connecting N AI client applications (code editors, custom dashboards, desktop agents) to M enterprise systems (GitHub, Postgres, Slack, Jira) required custom glue code for every client-tool pair.
MCP establishes a standardized client-server model over JSON-RPC 2.0, allowing any compliant client to connect to any MCP server out of the box.
Core primitives: Resources, Prompts, and Tools
MCP defines three core capabilities:
1. Resources: Read-only data streams exposed to the model (file contents, database rows). 2. Prompts: Pre-defined prompt templates managed server-side. 3. Tools: Executable actions the model can invoke with parameter schemas (executing SQL, sending API calls).
Security boundaries: What application engineers must still build
While MCP standardizes message formats, it delegates authentication, session permissioning, and rate limiting to host environments. Operating MCP servers in enterprise environments requires encapsulating them within existing IAM structures and boundary firewalls.