Model Context Protocol: Universal Connector for AI
How MCP from Anthropic solves the N×M integration problem and why it's becoming the standard for connecting AI agents to enterprise systems.
The N×M Integration Problem
If models are the brains of agentic AI, Model Context Protocol (MCP) is the universal nervous system connecting agents to the enterprise world. Introduced by Anthropic and released as open-source, MCP solves the 'N×M' integration problem – connecting N models to M tools.
Historically, connecting an LLM to a new tool (Google Drive, Slack, Linear) meant writing custom wrappers, managing authentication, and formatting API output for the specific model's context window. Each model-tool combination required unique code. As the number of models and tools grew, complexity increased exponentially.
MCP standardizes this with a unified JSON-RPC protocol. An MCP Server created for a PostgreSQL database can be instantly used in Claude Desktop, a LangGraph agent, or a PydanticAI script – without rewriting connector code. This transforms integration into a 'plug-and-play' experience.
Architecture and Three Types of Interactions
MCP creates a standardized schema for three key types of interactions that cover most enterprise needs:
- Tools: Executable functions like query_database, send_slack_message, create_jira_ticket
- Resources: Read-only data access via URI – file contents, logs, API responses
- Prompts: Predefined templates for standardized interaction with various systems
Building an Enterprise Skills Library
For specialized consulting firms, MCP is the technological foundation for 'Skills Building' service. Instead of writing custom Python functions for each client's database, you can develop a library of proprietary MCP Servers that are reusable across projects.
Examples of specialized servers: 'Accounting MCP' connects to Xero/QuickBooks APIs and exposes tools like get_unpaid_invoices or match_transaction. 'Legal MCP' connects to LexisNexis or internal contract repositories and exposes resources like contract_clauses or regulatory_updates. 'DevOps MCP' connects to AWS/Kubernetes for log reading and service restarts.
By reusing these tested skills via MCP, new client projects can be assembled quickly Lego-style instead of coding from scratch. Deployment time is dramatically shortened and error risk reduced.
Building a 'Private Brain-Trust'
For 'Enterprise AI Tools' service, MCP is the delivery mechanism. Instead of building a monolithic RAG application, you build an MCP Server serving the company knowledge base. This server exposes standard tools like search_policy_docs, get_client_history, and summarize_meeting_notes.
Employees can then access these tools via Claude Desktop (locally, securely) or through a custom internal chat interface. Consistent data access across the organization is ensured with a unified interface.
The network effect of MCP means you can leverage community-created servers for common tools while focusing on developing high-value, custom enterprise connectors.
Secure Deployment Patterns
Running agent tools on enterprise data requires strict security. MCP supports several security patterns for different data sensitivity levels.
- Local/Stdio: For high-security environments, MCP servers run locally as subprocesses, communicating via standard input/output. Data stays strictly within the security boundary.
- SSE/WebSockets: For distributed agents, MCP supports Server-Sent Events, enabling remote agents to connect to centralized data services securely via HTTPS with authentication.
- Least Privilege Principle: MCP servers can be configured with read-only access. An agent can read from the accounting database but only propose (not execute) payments.
- Granular Permissions: Each tool can have its own authorization rules and audit log.
Practical Example: Legal Guard System
The 'Legal Guard' architecture for internal directive compliance automation uses MCP as its backbone. The MCP server monitors 'EU Official Journal' (web source) and internal SharePoint (policy documents). Retriever uses Hybrid Search (Keyword + Vector) to obtain relevant internal policies.
Reasoner (DeepSeek R1) deeply analyzes conflict between new law and old policy, generates 'Compliance Gap Analysis'. Drafter (Claude Opus 4.5) proposes specific text changes. Human-in-the-Loop checkpoint ensures legal expert approves changes before deployment.
The entire system is built on reusable MCP components that can be deployed to other clients with minimal modifications.