The Model Context Protocol (MCP), open-sourced by Anthropic in November 2024, has quickly become the de facto cross-cloud interface for linking AI agents to tools, services and data in enterprise settings. Since its launch, leading cloud vendors and AI providers have introduced first-party MCP integrations, and independent platforms are building out support.
MCP uses JSON-RPC 2.0 to let AI systems—such as large language models—locate and invoke functions, tools, APIs or data stores offered by MCP-compatible servers. It removes the need for point-to-point connectors: once a tool speaks MCP, any MCP-aware agent or application can interact with it securely. Official SDKs exist for Python, TypeScript, C# and Java. Reference implementations cover databases, GitHub, Slack, Postgres, Google Drive, Stripe and more.
Who’s adopting MCP?
- Cloud providers: AWS (API MCP Server, MSK, Price List), Azure (AI Foundry MCP Server), Google Cloud (MCP Toolbox for Databases)
- AI platforms: OpenAI (Agents SDK, ChatGPT desktop), Google DeepMind (Gemini), Microsoft Copilot Studio, Claude Desktop
- Developer tools: Replit, Zed, Sourcegraph, Codeium
- Enterprise platforms: Block, Apollo, FuseBase, Wix embed MCP into AI assistant workflows
July 2025 updates from AWS included a developer preview of its API MCP Server for natural-language API calls, an MSK MCP Server with a standardized interface for Kafka metrics and cluster management (backed by IAM security, permissions and OpenTelemetry tracing), and a Price List MCP Server offering live pricing and availability by region. AWS also published a Code Assistant MCP Server, a Bedrock agent runtime and sample servers for rapid onboarding; all are open source where possible.
AWS integration steps:
- Deploy via Docker or ECS following AWS documentation
- Secure endpoints with TLS, Cognito, WAF and IAM roles
- Define API capabilities, issue credentials and connect agents
- Monitor with CloudWatch and OpenTelemetry; rotate credentials regularly
AWS stands out for its scalable infrastructure, the broadest first-party MCP coverage across services and its multi-region pricing and context APIs.
Azure’s AI Foundry MCP Server now unifies CosmosDB, SQL, SharePoint, Bing and Fabric under a single protocol, removing custom integration work. Copilot Studio can auto-discover and invoke these MCP functions, simplifying data flows and actions in Microsoft 365. SDKs in Python, TypeScript and community-maintained kits receive frequent updates.
Azure integration steps:
- Launch a server in Container Apps or Azure Functions
- Secure with TLS, Azure AD OAuth and RBAC
- Publish the agent to Copilot Studio or Claude and link to tools via MCP schemas
- Use Azure Monitor and Application Insights for observability
Deep ties to Microsoft’s productivity suite, enterprise-grade identity and governance plus no-/low-code agent options distinguish Azure.
On Google Cloud, the MCP Toolbox for Databases debuted in July 2025, letting teams connect to Cloud SQL, Spanner, AlloyDB, BigQuery and other stores in under ten lines of Python. Vertex AI’s Agent Development Kit (ADK) gained native MCP support for multi-agent workflows across tools and datasets. Security features include centralized connection pooling, IAM integration and VPC Service Controls.
GCP integration steps:
- Deploy the MCP Toolbox from Cloud Marketplace or as a managed microservice
- Secure network paths with IAM, VPC Service Controls and OAuth2
- Register tools and expose APIs for agents; invoke operations via Vertex AI or other MCP clients
- Audit activity in Cloud Audit Logs and enforce Binary Authorization
Google Cloud excels at data-tool integration, rapid agent orchestration and robust network controls.
Deployments face threats such as prompt injection that steers agent behavior, privilege misuse, tool poisoning, impersonation and shadow MCP rogue servers that pretend to be valid endpoints. Some MCP client libraries remain open to remote code execution by malicious servers. Operators should isolate MCP networks, restrict connections to trusted HTTPS hosts, sanitize AI inputs, validate all tool metadata, enforce signatures, rotate credentials regularly, audit privileges and maintain detailed logs for forensic analysis.
In July 2025, CVE-2025-53110 and CVE-2025-6514 exposed remote code execution risks in some MCP client libraries triggered by malicious servers. Users must update affected libraries and block untrusted endpoints immediately.
Official SDKs for Python, TypeScript, C# and Java are updated regularly. Third-party packages for Go and Ruby have appeared, expanding language options. GitHub sample servers for common tools draw significant developer interest and contributions.
Anthropic maintains core reference servers for Postgres, GitHub, Slack and Puppeteer with rapid releases. OpenAI provides full MCP support in GPT-4o, the Agents SDK and ChatGPT environments, complete with extensive tutorials. Google DeepMind’s Gemini API embeds MCP definitions for enterprise and research. Netflix uses MCP for internal data workflows, Databricks for pipeline automation, and Docusign and Litera automate legal contracts. Tools like Replit, Zed, Codeium and Sourcegraph offer live code context. Block, Apollo, FuseBase and Wix integrate MCP into next-generation enterprise applications.
MCP remains the preferred open standard for AI-to-tool communication. AWS, Azure and Google Cloud each deliver native MCP support with secure enterprise patterns. Leading AI and developer platforms have become early proponents of this protocol. Security challenges persist, so follow zero-trust practices, update components and enforce strict access controls. MCP provides a streamlined method for maintainable AI-driven workflows without custom connectors for every agent and service.