The Model Context Protocol (MCP), open source by Anthropic in November 2025, quickly became the cross -standard to connect AI agents to the tools, services and data in the company landscape. Since its release, the main cloud suppliers and the main IA suppliers have sent the first part MCP integrations, and independent platforms quickly widen the ecosystem.
1. MCP and ecosystem overview
What is MCP?
- MCP is an open standard (based on JSON-RPC 2.0) which allows AI systems (such as large language models) to discover and call functions, tools, APIs or data stores exposed by any MCP compatible server.
- It was specially designed to eliminate the problem of the “N × M” connector in tool integrations: once a MCP tool, any The agent or application that supports MCP can interface with it safely and predictable.
- Official SDK: Python, Dactylographed, C #, Java. Reference servers exist for databases, GitHub, Slack, Postgres, Google Drive, Stripe, and more.
Who adopts MCP?
- Cloud suppliers: AWS (API MCP Server, MSK, Price List), Azure (AI Foundry MCP Server), Google Cloud (MCP Toolbox for databases).
- AI platforms: OPENAI (SDK agents, Chatgpt Desktop), Google Deepmind (Gemini), Microsoft Copilot Studio, Claude Desktop.
- Developer tools: Replies, zed, sourcegraph, codeium code.
- Company platforms: Block, Apollo, Fusebase, Wix – Ense of the integration of MCP to integrate AI assistants into personalized commercial workflows.
- Ecosystem growth: The global MCP server market is expected to reach $ 10.3 billion in 2025reflecting the rapid adoption of companies and the maturity of the ecosystem.
2. AWS: Cloud MCP
What's new (July 2025):
- AWS API MCP Server: Developer overview was launched in July 2025; Allows MCP compatible AI agents to call any AWS API via natural language in completeness.
- Amazon MSK MCP Server: Now provides a standardized linguistic interface to monitor Kafka measurements and manage clusters via agent applications. Integrated security via IAM, fine grain authorizations and openlemmetry tracing.
- List of MCP Server Price: AWS price and availability in real time – Request rates by region on demand.
- Additional offers: MCP Server Assistant Code, Roundrock Agent Runtime and Example of servers for rapid interest. All are open source where this is possible.
Integration steps:
- Deploy the desired MCP server using Docker or DHW, taking advantage of official AWS advice.
- Harden the evaluation criteria with TLS, Cognito, Waf and IAM roles.
- Define the visibility / capacities of the API – EG,
msk.getClusterInfo. - Make OAUTH tokens or IAM identification information for secure access.
- Connect with AI customers (Claude Desktop, Openai, Bouetter, etc.).
- Via Cloudwatch and OpenTelemery for Observability.
- Regularly rotate references and regularly revise access policies.
Why AWS leads:
- Unparalleled scalability, official care for the widest set of AWS services and Pricing / Context API Multi-Regions with fine grain.
3. Microsoft Azure: MCP in Copilot & Ai Foundry
What's new:
- Azure Ai Foundry MCP Server: The unified protocol now connects Azure Services (Cosmosdb, SQL, SharePoint, Bing, Fabric), releasing the developers of the personalized integration code.
- Copilot Studio: Discover and invoke MCP capabilities transparently, which makes it easy to add new data or actions to the Microsoft 365 workflows.
- SDKS: Python, typescript and community kits receive regular updates.
Integration steps:
- Create / launch an MCP server in Azure container applications or Azure functions.
- Secure termination points using TLS, Azure AD (OAUTH) and RBAC.
- Publish the agent for Copilot Studio or Claude Integration.
- Connect to Backend tools via MCP diagrams: Cosmosdb, Bing API, SQL, etc.
- Use the Azure monitor and application information for telemetry and safety monitoring.
Why Azure stands out:
- Deep integration with the Microsoft productivity suite, business quality identity, governance and activation of codes without codes.
4. Google Cloud: MCP Toolbox & Vertex AI
What's new:
- MCP Toolbox for databases: Released in July 2025, this open source module simplifies AI -AI -AGENT access to Cloud SQL, SPANNER, ALLOYDB, BIGQUERY, and even more – reducing integration to <10 lines of Python code.
- Vertex ai: Native MCP via the agent development kit (ADK) allows robust multi-agent workflows between tools and data.
- Safety models: POOLOOD CENTRALISE CONNECTION, IAM integration and VPC services orders.
Integration steps:
- Launch MCP Toolbox from Cloud Marketplace or deploy in managed microservice.
- Security with IAM, VPC Service Controls and OAUTH2.
- Record MCP tools and expose APIs for the consumption of AI agents.
- Invoke database operations (for example,
bigquery.runQuery) Via Vertex AI or LLMS Compatible MCP. - Audit all access via cloud audit newspapers and binary authorization.
Why GCP excels:
- Integration of the best data tools, fast and solid hygiene agent orchestration of the business network.
5. Best cross -practices
6. Safety and risks management (2025 landscape of threats)
Known risks:
- Rapid injection, abuse of privileges, tool poisoning, identity theft, Shadow MCP (Rogue server) and new vulnerabilities allowing the execution of the remote code in certain MCP customer libraries.
- Mitigation: Connect only to Trust MCP servers on HTTP.
Recent vulnerabilities:
- July 2025: CVE-2025-53110 and CVE-2025-6514 highlight the risk of execution of the remote code from malicious MCP servers. All users must update affected libraries and restrict exposure to public / unreliable MCP termination points.
7. Expanded ecosystem: beyond “three big”
- Anthropic: Basic reference MCP servers – Postgres, GitHub, Slack, Puppeteer. Maintains rapid versions with new capacities.
- OPENAI: Complete MCP management in GPT-4O, agents SDK, sandbox and use of production; Large tutorials now available.
- Google Deepmind: The Gemini API has the management of the native SDK for MCP definitions, expanding the coverage of corporate and research scenarios.
- Other companies adopting MCP:
8. Example: AWS MSK MCP integration flow
- Deploy AWS MSK MCP Server (Use an official AWS GitHub sample).
- Secure with Cognito (Oauth2), WAF, IAM.
- Configure the actions of the API available and the rotation of the tokens.
- Connect the agent AI supported (Claude, Openai, Souilture).
- Use agent invocations, for example,
msk.getClusterInfo. - Watch and analyze with Cloudwatch / OpenTelemetry.
- Iterer by adding new APIs of tools; apply the least privileges.
9. Summary (July 2025)
- MCP is the main open standard for a-tool integrations.
- AWS, Azure and Google Cloud each offer a robust first partial, often open source MCP support, with secure corporate models.
- The main platforms of AI and developers (Openai, Deepmind, Anthropic, Relit, Sourcegraph) are now the MCP “First Movers” ecosystem.
- Security threats are real and dynamic: up -to -date tools, use Zero Trust and follow best practices for managing identification information.
- MCP unlocks rich and maintained agent workflows without personalized API by agent or tool.
Michal Sutter is a data science professional with a master's degree in data sciences from the University of Padova. With a solid base in statistical analysis, automatic learning and data engineering, Michal excels in transforming complex data sets into usable information.
