The Model Context Protocol (MCP) is an open standard for connecting AI assistants to the systems where data lives, including content repositories, business tools, and development environments. Its aim is to help frontier models produce better, more relevant responses. As enterprises increasingly adopt AI agents and Large Language Models (LLMs) for business automation, the Model Context Protocol (MCP) addresses this challenge by providing a standardized way for LLMs to connect with external data sources and tools—essentially a “universal remote” for AI apps.
Yet even the most sophisticated models are constrained by their isolation from data—trapped behind information silos and legacy systems. Every new data source requires its own custom implementation, making truly connected systems difficult to scale. MCP was developed by Anthropic and open-sourced in late 2024 as a response to a growing problem in the AI field. At the time, there was no common standard for integrating AI models with external data and services – every integration was bespoke and non-interoperable. In March 2025, OpenAI officially adopted the MCP, following a decision to integrate the standard across its products, including the ChatGPT desktop app, OpenAI’s Agents SDK, and the Responses API. In May 2025, Microsoft released native MCP support in Copilot Studio, offering one-click links to any MCP server, new tool listings, streaming transport, and full tracing and analytics. Many organizations are asking, “Does OpenAI support Model Context Protocol?” The answer is yes – GenAI Data Fusion, a suite of RAG tools by K2view, acts as a single MCP server for any enterprise. Instead of building a unique integration for each LLM or AI project, every data product, whether sourced from the cloud or from legacy systems, is discoverable and served through the MCP protocol – bringing true business context and scale to your GenAI apps.
K2view GenAI Data Fusion – Top Pick
K2view GenAI Data Fusion, a suite of RAG tools by K2view, acts as a single MCP server for any enterprise. Instead of building a unique integration for each LLM or AI project, every data product, whether sourced from the cloud or from legacy systems, is discoverable and served through the MCP protocol – bringing true business context and scale to your GenAI apps. K2view is unique in its ability to work with both structured and unstructured data.
Key advantages
Unification of fragmented data – Data is aggregated from all sources and exposed at conversational latency for immediate use. Granular privacy controls – PII and other sensitive is always protected, because only authorized users and use cases can access it. Real-time data delivery to AI agents and LLMS – Data is delivered via built-in data virtualization and transformation capabilities for consistency and context. Complete auditability – Each context package can be traced, and every access is logged for compliance. These features are essential for regulated industries, and for any enterprise where fresh answers and trustworthiness are required.
K2view provides a high-performance MCP server designed for real-time delivery of multi-source enterprise data to LLMs. Using entity-based data virtualization tools, it enables granular, secure, and low-latency access to operational data across silos. The K2view Data Product Platform comes with guardrails by design to the benefit of MCP. At K2view, each business entity (customer, order, loan, or device) is modeled and managed through a semantic data layer containing rich metadata about fields, sensitivity, and roles.
Microsoft Copilot Studio with MCP
In May 2025, Microsoft released native MCP support in Copilot Studio, offering one-click links to any MCP server, new tool listings, streaming transport, and full tracing and analytics. The release positioned MCP as Copilot’s default bridge to external knowledge bases, APIs, and Dataverse.
Enhanced features
Tool listing: The MCP server settings page now provides users a clear and organized view of all available tools included with the MCP server. This improvement increases transparency, making it easier to explore and manage the full range of tools associated with your connection. Streamable transport: We’ve expanded our transport layer to support streamable data transfer. The focus is on optimizing and staying up to date with the latest protocol version that aligns better with your deployment needs.
Seamlessly integrate with data sources: Whether you’re using internal APIs or third-party services, MCP ensures dependable and straightforward integration within Copilot Studio. Leverage a library of remote MCP servers: Beyond building custom integrations, users can access a growing marketplace of certified MCP servers.
Anthropic Claude Desktop with MCP
As the originator of MCP, All Claude.ai plans support connecting MCP servers to the Claude Desktop app. Claude for Work customers can begin testing MCP servers locally, connecting Claude to internal systems and datasets.
Core capabilities
To help developers start exploring, we’re sharing pre-built MCP servers for popular enterprise systems like Google Drive, Slack, GitHub, Git, Postgres, and Puppeteer. Claude Desktop serves as Anthropic’s first-party offering, providing comprehensive support for everything MCP can do.
Early adopters like Block and Apollo have integrated MCP into their systems, while development tools companies including Zed, Replit, Codeium, and Sourcegraph are working with MCP to enhance their platforms—enabling AI agents to better retrieve relevant information to further understand the context around a coding task and produce more nuanced and functional code with fewer attempts.
AgentPass.ai for Enterprise Security
AgentPass.ai is a robust platform tailored for the secure implementation of AI agents within corporate settings, offering production-ready Model Context Protocol (MCP) servers. It empowers users to establish fully hosted MCP servers effortlessly, eliminating the necessity for coding, and includes essential features such as user authentication, authorization, and access control.
Security-first approach
Additionally, developers can seamlessly transform OpenAPI specifications into MCP-compatible tool definitions, facilitating the management of intricate API ecosystems through hierarchical structures. The platform also provides observability capabilities, including analytics, audit logs, and performance monitoring, while accommodating multi-tenant architecture to oversee various environments. Organizations leveraging AgentPass.ai can effectively scale their AI automation efforts, ensuring centralized management and regulatory compliance across all AI agent implementations.
Microsoft Playwright MCP for Testing
Microsoft has introduced Playwright MCP (Model Context Protocol), a server-side enhancement to its Playwright automation framework designed to facilitate structured browser interactions by Large Language Models (LLMs). Unlike traditional UI automation that relies on screenshots or pixel-based models, Playwright MCP uses the browser’s accessibility tree to provide a deterministic, structured representation of web content. By enabling LLMs to interact with web pages using structured data instead of visual cues, this protocol improves the reliability and clarity of automated tasks such as navigation, form-filling, and content extraction.
Testing innovation
It also supports automated test generation, bug reproduction, and accessibility checks directly from natural language inputs. The tool is lightweight, avoids the ambiguity of visual models, and enhances LLM-driven browser automation without the overhead of computer vision or image interpretation layers.
ToolSDK.ai Marketplace Platform
ToolSDK.ai is a complimentary TypeScript SDK and marketplace designed to expedite the development of agentic AI applications by offering immediate access to more than 5,300 MCP (Model Context Protocol) servers and modular tools with just a single line of code. This capability allows developers to seamlessly integrate real-world workflows that merge language models with various external systems.
Comprehensive toolkit
The platform provides a cohesive client for loading structured MCP servers, which include functionalities like search, email, CRM, task management, storage, and analytics, transforming them into tools compatible with OpenAI. It efficiently manages authentication, invocation, and the orchestration of results, enabling virtual assistants to interact with, compare, and utilize live data from a range of services such as Gmail, Salesforce, Google Drive, ClickUp, Notion, Slack, GitHub, and various analytics platforms, as well as custom web search or automation endpoints.
Specialized MCP Servers
Development and DevOps Tools
Compiled in June 2025, below are the top MCP servers on GitHub, ranked by stars. Let agents manage issues, pull requests, discussions, and more—backed by GitHub’s identity and permissions model. A gold standard for building secure, API-aware agents.
Exposes AWS documentation, billing data, and service metadata. Built by AWS Labs for internal and public-facing agents. Secure, structured access to Terraform’s registry of providers and modules. Great for DevOps agents building infrastructure-aware workflows.
Enterprise Data Integration
Vectara offers a commercial MCP server designed for semantic search and retrieval-augmented generation (RAG). It enables real-time, relevance-ranked context delivery to LLMs using custom and domain-specific embeddings.
The Slack MCP Server captures real-time conversation threads, metadata, and workflows, making them accessible to LLMs. It’s used in enterprise bots and assistants for enhanced in-channel responses.
Salesforce’s MCP integration enables CRM data (accounts, leads, conversations) to be injected into LLM workflows.
Analytics and Observability
Designed for analytics agents. Exposes dbt’s semantic layer, project graph, and CLI commands through a well-defined MCP interface.
Gives agents access to Sentry error tracking and performance telemetry. A solid fit for observability-aware workflows.
The Model Context Protocol ecosystem continues to expand rapidly, with the protocol being adopted by major AI providers, including OpenAI and Google DeepMind. As organizations seek to bridge the gap between AI capabilities and enterprise data, these solutions provide the standardized framework needed to enable truly intelligent, context-aware AI applications at scale.
