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Natoma MCP Platform
Effortlessly integrate AI agents with your tools and data

What is Natoma MCP Platform?

Accelerate AI development and deployment using a hosted platform designed for seamless integration of AI agents with enterprise tools and data. This platform offers one-click access to over 100 pre-built and fully managed Model Context Protocol (MCP) servers, simplifying the connection process and eliminating the need for complex server maintenance.

Enhance reliability and security through built-in versioning, fine-grained authorization based on user roles, and robust data protection measures both in transit and at rest. The platform supports leading Large Language Model (LLM) architectures and custom MCP servers, providing flexibility for complex AI projects. Ensure compliance and maintain oversight with granular access policies and comprehensive audit logs.

Features

  • One-Click Integration: Access over 100+ fully managed Model Context Protocol (MCP) servers with a single click.
  • Verified Servers: Utilize verified and maintained MCP servers for reliable integration.
  • Easy Deployment: Configure and deploy integrations effortlessly with one-click deployment and intuitive setup.
  • Increased Reliability: Benefit from built-in versioning for enhanced stability and reduced maintenance.
  • Broad Compatibility: Supports leading LLM architectures and custom MCP servers.
  • Fine-Grained Authorization: Control user access based on roles and responsibilities.
  • Secure Data Handling: Safeguard data in transit and at rest using robust security controls.
  • Policy Controls & Audit Logs: Establish access policies and ensure compliance with detailed reporting and logs.
  • Scalable Management: Effortlessly scale and manage deployments with automation and infrastructure-as-code capabilities.

Use Cases

  • Integrating AI agents with existing enterprise tools and data.
  • Accelerating the development cycle for AI-powered applications.
  • Building secure and scalable agentic AI solutions for businesses.
  • Connecting AI agents to diverse data sources and APIs via MCP.
  • Managing AI deployments with fine-grained access control and security policies.
  • Facilitating enterprise adoption of agentic AI technologies.

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