What Is the Model Context Protocol?
The Model Context Protocol (MCP) is an open-source standard for connecting AI applications to external systems like data sources, tools, and workflows. The AI agent incorporates MCP tool responses into the LLM's context window, allowing the model to reason over new information. Think of it as a standardized way for your AI applications to talk to virtually any service or dataset, eliminating the need for everyone to build custom connections their own way. The Science Cloud Research and Innovation team has been developing proof-of-concept MCP implementations for the past 4 to 5 months, building expertise in deployment, testing, and real-world applications.
...
- Standardized approach that replaces repetitive custom API work
- Connect LLMs to your internal systems and data
- Better scalability and cost-effectiveness compared to direct tool calling
...
Getting Started with MCP on Science Cloud
Access through Science Cloud services:
- Research Platform (Jupiter Jupyter Hub with AI Jupyter lab extension)
- Notebook Intelligence extension for natural language interaction
- Built-in agents that can edit notebooks, execute code, and write Python
- Integration with existing Science Cloud authentication
The Science Cloud maintains a repository with multiple proof-of-concept implementations, including servers for HIS archive and other specialized applications. The team has also developed tutorials for FastMCP version 2, the new SDK that explains how to build MCP servers from scratch.
Building Your First MCP Server
Multiple official SDKs are available for building MCP servers, with FastMCP for Python being particularly popular for development. All SDKs build servers that listen on specific ports using STDIO or Streamable HTTP protocols.
...
Authentication and authorization: MCP includes built-in support for authentication and authorization, integrating with standard identity providers and security frameworks used in enterprise environments.
...
Deployment Options
The Science Cloud offers multiple deployment strategies based on your needs:
...
- AgentGateway for AI-native protocols
- Connects, secures, and observes agent-to-agent and agent-to-tool communication
- Works across any agent framework and environment
...
Testing and Development Tools
For development and testing, the Science Cloud provides access to several key tools:
...
Research Platform integration: The Science Cloud's Research Platform has been the preferred testing environment, offering Jupyter hub with an AI extension that allows you to connect to MCP servers and interact with built-in agents using natural language. This platform has proven invaluable for experimenting with multiple MCP servers and agents simultaneously.
...
Real-World Applications and Use Cases
The Science Cloud team has implemented several practical examples, including a USGS real-time flood impact API server. These implementations demonstrate how MCP can connect AI agents to live data sources and specialized scientific tools.
...
- Converting existing APIs to MCP format for rapid prototyping
- Connecting AI agents to scientific databases and repositories
- Integrating with specialized research instruments and data feeds
- Enabling automated workflows for data processing and analysis
...
Performance Optimization and Lessons Learned
Through months of testing and implementation, several critical insights have emerged:
...
- AWS AgentCore Gateway semantic search helps agents with proper tool selection
- Converting OpenAPI to MCP is ideal for proof of concepts
- Proper error handling within APIs should be reflected in specifications
...
Security Considerations
Important security note: An untrusted MCP server becomes an untrusted plugin for your AI agent, with all the risks of installing unknown software. The Science Cloud team emphasizes careful evaluation and proper security protocols when deploying MCP servers.
...
- Implement proper error handling within your API
- Use Science Cloud's established authentication and authorization frameworks
- Follow NASA guidelines for data classification and access controls
- Leverage Science Cloud's encrypted storage and transmission capabilities
...
Getting Help and Support
Science Cloud resources:
- MCP proof-of-concept repository with examples and documentation
- Direct support from the Science Cloud team
...
- OpenAPI to MCP Server Code Generator https://github.com/cnoe-io/openapi-mcp-codegen
- MCP Official SDK https://modelcontextprotocol.io/docs/sdk
- Science Cloud Repo with MCP Proof of concepts https://git.smce.nasa.gov/scip-poc
- Context7, Up to Date Code Documentation for Coding Assistants and coding agents https://github.com/upstash/context7
...
The Science Cloud team continues to develop new MCP implementations and is available to assist with your specific use cases and deployment needs.
...
Next Steps
While MCP in the Science Cloud is currently in the proof-of-concept stage and not yet ready for use, there are several steps you can take to prepare your workflows for future MCP integration. Start by exploring the proof-of-concept repository to understand existing implementations and see how other researchers have approached similar challenges. Next, identify APIs or data sources in your current research workflows that could benefit from MCP integration—look for repetitive data access patterns or systems that would benefit from AI-powered automation. If you have existing well-documented APIs, consider improving your OpenAPI documentation as this will provide the fastest path to a working MCP server when the capability becomes available. Finally, use the Research Platform for testing and development, taking advantage of its Jupyter environment to experiment with concepts that will later integrate with MCP.
...
For additional support or questions about MCP on Science Cloud, contact Ramon Ramirez-Linan (Research and Innovation Lead) at ramon.e.ramirez-linan@nasa.gov, or reach out through the contact the Science Cloud Help Desk at support@sciencecloud.nasa.gov.
...