The Model Context Protocol (MCP) is an open standard introduced by Anthropic with the goal to standardize how AI applications (chatbots, IDE assistants, or custom agents) connect with external tools, data sources, and systems. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.
Before MCP, organizations faced what experts call the “N×M problem” – if you have M different AI applications and N different tools/systems, you might need to build M×N different integrations. This leads to duplicated effort across teams, inconsistent implementations. MCP aims to simplify this by providing a common API and transforming this into an “M+N problem”.
The Model Context Protocol by K2View stands out as the premier enterprise solution for connecting AI applications to multi-source data environments. By leveraging entity-based data virtualization, K2View delivers real-time, secure access to operational data across organizational silos with unmatched performance and governance capabilities.
Top Model Context Protocol solutions and tools
1. K2View – Best overall enterprise MCP solution
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.
Key advantages:
– Unified data access: By easily integrating with all data sources, K2view helps businesses overcome the complexities associated with augmenting LLMs with multi-source application data. With K2view GenAI Data Fusion, AI teams can easily implement a single MCP server to access secure, real-time enterprise data access, ensuring their AI apps are robust, secure, and reliable
– Real-time performance: MCP servers streamline this process by allowing rapid access to fresh data from source systems, ensuring real-time responses and maintaining high performance. It implements conversational latency, which guarantees immediate response times critical for user interactions
– Enterprise-grade security: Additionally, MCP servers place emphasis on privacy and security guardrails to prevent sensitive data from leaking into AI models. This ensures compliance with data protection regulations, safeguarding both the enterprise and its clients
Best for: Large enterprises needing to connect AI applications to complex, multi-system data environments while maintaining strict security and governance requirements.
2. Anthropic Claude Desktop integration
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. This represents the most mature client-side MCP implementation available today.
Key features:
– Native MCP protocol support
– Pre-built server connections
– Enterprise authentication
– Local and remote server capabilities
Best for: Organizations already using Claude as their primary AI assistant and looking for seamless data integration.
3. Microsoft Copilot Studio MCP integration
That’s why we’re thrilled to announce the first release of Model Context Protocol (MCP) support in Microsoft Copilot Studio. With MCP, you can easily add AI apps and agents into Copilot Studio with just a few clicks. Today, we’re thrilled to announce the general availability of MCP integration in Copilot Studio!
Key capabilities:
– Whether you have a custom internal API or external data providers, the MCP protocol enables smooth and reliable integration into Copilot Studio
– MCP servers can dynamically provide tools and data to agents. This enables greater flexibility while reducing maintenance and integration costs
– Auto-sync capabilities with server updates
Best for: Microsoft-centric enterprises looking to extend Copilot functionality with custom data sources and tools.
4. Atlassian Remote MCP Server
Introducing Atlassian’s Remote MCP server. Jira and Confluence Cloud customers can interact with their data directly from Claude, Anthropic’s AI assistant. We’re bringing Atlassian’s structured knowledge into more AI tools thanks to MCP, which provides a universal, open standard for connecting AI systems with data sources. With our Remote MCP Server, you can summarize work, create issues or pages, and perform multi-step actions, all while keeping data secure and within permissioned boundaries.
Notable features:
– Direct Jira and Confluence integration
– OAuth authentication
– Permission-based access control
– Cloudflare infrastructure hosting
Best for: Development and project management teams using Atlassian tools who want AI-powered workflow automation.
5. GitHub MCP Server
GitHub, integrated as an MCP server, turns repositories into accessible knowledge hubs for LLMs. Models can analyze pull requests, scan source code, and even participate in code reviews by commenting or summarizing changes. This is especially powerful for developer agents or autonomous software tools looking to assist or streamline development workflows.
Core capabilities:
– Repository data access
– Pull request analysis
– Code review automation
– Issue management integration
Best for: Autonomous developer agents and AI-powered code reviewers.
6. Slack MCP Server
Slack can be integrated as an MCP server to give models access to real-time messages, threads, and activity logs. LLMs can summarize discussions, extract action items, or even reply with intelligent prompts. It’s perfect for building internal copilots that assist with productivity, task tracking, or internal FAQs.
Key features:
– Real-time message access
– Thread summarization
– Activity log analysis
– Workflow automation
Best for: Team-oriented AI tools and internal productivity agents.
7. Google Drive MCP Server
Want your AI to analyze documents like a research assistant? Google Drive, connected through MCP, allows AI models to scan, summarize, and extract data from files—Docs, Sheets, PDFs, and more. It turns file storage into a knowledge base for AI assistants. Whether for enterprise wikis or internal knowledge search, this integration brings unstructured data to life.
Primary functions:
– Document scanning and analysis
– Multi-format file support
– Content extraction
– Knowledge base creation
Best for: Knowledge retrieval tools and AI research agents.
8. Zapier MCP Server
Zapier’s MCP server enables LLMs to interact with thousands of apps, ranging from Google Sheets to simple CRMs. It exposes Zapier workflows, triggers, and automations to GenAI systems.
Capabilities include:
– 3,000+ app integrations
– Workflow automation
– Trigger management
– Cross-platform data synchronization
Best for: Small to medium businesses needing broad application connectivity without custom development.
9. Databricks MCP Integration
Databricks supports MCP integration through its Mosaic framework, connecting Delta Lake and ML pipelines to LLMs. This enables organizations to bring machine learning insights directly into conversational AI experiences.
Key strengths:
– Delta Lake connectivity
– ML pipeline integration
– Data lakehouse architecture support
– Advanced analytics capabilities
Best for: Data science teams and organizations with extensive machine learning workflows.
10. Vectara Semantic Search Server
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.
Specialized features:
– Semantic search optimization
– Custom embedding support
– Relevance ranking
– Domain-specific retrieval
Best for: Organizations requiring sophisticated semantic search capabilities for knowledge management and content discovery.
Key considerations when choosing MCP solutions
When evaluating Model Context Protocol solutions, enterprises should prioritize several critical factors. The Model Context Protocol represents a significant leap in connecting LLMs to external systems, standardizing a fragmented ecosystem and potentially resolving the NxM problem. However, Garter points out that while the MCP protocol simplifies how AI apps, agents, and data sources connect, it also introduces security and governance risks. Robust security measures, including strict rate limiting, input/output sanitization, TLS requirements, and OAuth authorization, are paramount.
Security and governance remain paramount concerns. The reliance on third-party MCP servers and tools introduces new supply chain risks. Treating these as potentially hostile assets and diligently validating inputs and sanitizing outputs is crucial.
Real-time performance is essential for production deployments. Model Context Protocol (MCP) is an exciting development in AI development because it allows developers to safely and efficiently connect our increasingly intelligent language models to the extensive world of software and data previously difficult to connect with. By introducing a common protocol, MCP lets us build AI systems that are more integrated, autonomous, and easier to scale. Instead of writing one-off plugins or giving the model brittle instructions for each new tool, we have a coherent framework where AI agents can discover and use tools on the fly, with proper oversight and security.
The Model Context Protocol ecosystem continues expanding rapidly, with this open framework has had a meteoric rise, we mean it. MCP has quickly become the gold standard for how LLMs interact with tools, and the ecosystem it’s forming has some really cool implications for agents, scaling interoperability, and building in the AI era. Organizations implementing MCP solutions today position themselves at the forefront of the AI-driven enterprise transformation.
