Your AI models are isolated from the business data and tools they need, requiring bespoke one-off connectors for every integration.One protocol, all your systems.

The Model Context Protocol (MCP) is an open standard developed by Anthropic that defines a uniform interface for connecting AI models to external tools, data sources, and services. Before MCP, every AI integration was bespoke: a different connector for each tool, different authentication patterns, different error handling, and no shared schema — creating a fragmented, unmaintainable web of point-to-point integrations. MCP provides a single, versioned protocol with typed tool definitions, standardised resource descriptions, and a consistent transport layer (stdio, HTTP with SSE, or WebSocket). For enterprise AI, this matters because it means AI agents and LLM-powered applications can connect to new data sources and tools through a shared interface rather than custom code per integration. An MCP server you build for your CRM today can be reused by any MCP-compatible AI client — including Claude, Continue, Cursor, and any application built on MCP-compatible frameworks — without modification.

Any system with a programmatic interface can be wrapped in an MCP server. The most common enterprise integrations in production deployments are: CRM systems (Salesforce, HubSpot — exposing contact lookup, deal status, activity logging as MCP tools), databases (PostgreSQL, MySQL, BigQuery — read-only query interfaces scoped to specific schemas, with row-level security enforced at the MCP layer), ticketing and project management (Jira, ServiceNow, Linear — issue creation, status retrieval, comment posting), document and knowledge stores (Confluence, SharePoint, Notion — search, retrieve, and update pages), communication platforms (Slack, Teams — message posting, channel listing, user lookup), ERP systems (SAP, Oracle — inventory queries, purchase order status, financial data reads), and internal REST and GraphQL APIs. The MCP server acts as the single, controlled entry point — the AI model never has direct access to the underlying system.

Authentication and authorisation in an MCP server are implemented at the server layer, not delegated to the AI model or client. The MCP server authenticates to each upstream system using its own service credentials (OAuth client credentials, API keys stored in a secrets manager, or mTLS certificates) — the AI client never sees these credentials. For user-scoped access (where operations should be performed as the logged-in user rather than a service account), the MCP server implements OAuth 2.0 token exchange: the client presents a short-lived user token, the server validates it and maps it to the appropriate upstream permission scope. Fine-grained authorisation is enforced at the tool level — each MCP tool definition declares which user roles may invoke it, and the server validates this before executing the upstream call. All tool calls are logged with the requesting identity, timestamp, inputs, and outputs for audit.

Off-the-shelf MCP connectors exist for common public services (GitHub, Slack, Google Drive, Brave Search) and are suitable when your use case matches the default tool set and permission model. Custom MCP server development is required when: your system has a private API with no public MCP server available, you need to enforce enterprise-specific authorisation rules (row-level security, data classification filters, tenant isolation), you need to compose multiple upstream calls into a single atomic tool (e.g., a "create customer" tool that writes to CRM, sends a welcome email, and creates a billing record atomically), you need to transform or sanitise data between the AI model and the upstream system (removing PII before it reaches the model, translating legacy field names to a canonical schema), or you need to operate behind a corporate firewall with no public internet exposure. The specialist assesses your integration requirements and determines which components can use community servers versus require custom development.

MCP server testing uses a layered approach. Unit tests validate each tool handler: given a typed input, assert the correct upstream API call is made and the response is correctly transformed into the MCP result schema. Integration tests run against a staging instance of each connected system with realistic data, verifying authentication, authorisation enforcement, and error handling for every tool. Contract tests validate that the MCP server's tool definitions conform to the MCP specification version and that schema changes are backward-compatible. In production, monitoring captures: tool call latency and error rates (exposed as Prometheus metrics or pushed to your observability stack), upstream system error responses (5xx rates from each connected system surfaced as alerts), authentication failures (potential token expiry or permission drift), and unusual call patterns (unexpectedly high call volumes that may indicate a runaway agent or misuse). Structured logs of every tool invocation feed into your SIEM for security monitoring.