Simplifying Amazon Neptune Integration with MCP Servers

• Installing and configuring Neptune MCP servers
• Using the neptune-query server to interact with your Neptune database from an MCP-enabled Generative AI application
• Considerations when using these MCP servers

Prerequisites

• To run these servers, you must install uv following the directions here. You will also need to install Python 3.12 using uv python install 3.12
• An MCP client - There are a variety of MCP client applications available such as Cursor, Cline, Claude Code, and many others that support adding MCP servers, each with varying degrees of support. While each of these has their own specific uses and advantages, in this post I will be using Anthropic’s Claude Desktop to demonstrate how you can leverage these servers.
• An Amazon Neptune Database or an Amazon Neptune Analytics graph - Verify that your MCP client has network access to the Neptune endpoint for your graph/cluster
• The AWS CLI with appropriate credentials configured as the MCP server uses the credentials chain of the CLI to provide authentication. Please refer to these directions for options and configuration.

Installation and Setup

Using these MCP Servers

Now that we have configured our MCP server(s) let’s start up our client and verify that everything is working. With our client is running, let's check what we have to work with. In MCP, we have primatives called "tools" - think of them as special features that can perform specific actions. Let's start by asking our client what tools we have available and what each one can do. This will give us a good idea of all the different ways we can interact with our server.

Awesome, looks like our client has successfully connected to the server. Let's take a look at our graph's schema. One of the powerful things about MCP servers is how easily they work together with other client features. In this case, we're going to use the client to do three things in one go: pull up our graph's schema, create a visual representation of it, and give us a summary of how the graph is structured.

For this example, we're working with an air routes dataset. This includes information about locations, airports, and flight routes. Let's see how all these pieces fit together in our graph.
Since we're working with an LLM, let's put it to work helping us explore our data. Let's ask our client to suggest some interesting questions we might want to ask about our air routes graph. This is a nice way to get some ideas about what kind of information we can extract from our data.

Now that we have these suggested questions, let's put them to use. We can ask the LLM to create a query for any of these questions, and then run it against our Neptune database. This saves us from having to write the query ourselves - the LLM can translate our plain English question into the database query language for us.

Let’s take a moment to look at what just happened - the LLM did three things for us. First, it created the query. Then it ran that query to get the data from our Neptune database. Finally, it gave us a clear summary of what it found. If you're curious about the technical details, you can see the actual query that the LLM came up with to get this information from the database.

Just a quick note about queries - while we've been using openCypher in our examples, that's not your only choice. If you prefer, you can also use TinkerPop Gremlin queries. Our setup works great with both, so don't feel locked into one or the other. Pick whichever you know best or whichever makes more sense for what you're trying to do. The flexibility is there, so use what works for you!

If you check both results, you'll notice they give us the same information, just using different query languages.
This really shows how the Neptune MCP server simplifies working with Amazon Neptune graphs. Without touching any code, we were able to:

• Get a clear picture of our graph's structure, both visually and in text form
• Figure out what kind of information our graph contains and what questions it can help answer
• Create and run queries using either openCypher or Gremlin - whatever works best for you

Considerations when using MCP servers

Security First

• Code Review: Always conduct thorough security reviews of MCP-generated code before deploying to any environment
• Review Server code before Use: MCP servers provide AI tools access to your external data sources. However, this also exposes a variety of security challenges that require rigor and analysis to mitigate. I recommend reviewing the recommendations found in this paper for more information: Enterprise-Grade Security for the Model Context Protocol (MCP): Frameworks and Mitigation Strategies
• Credential Management: Implement least-privilege access principles when configuring AWS credentials for MCP servers. Misconfigured credentials can allow a server a higher level of access to your graph, such as allowing mutation queries, which can cause unintended consequences.
• Security Scanning: Integrate automated security scanning tools into your CI/CD pipeline to evaluate MCP-generated infrastructure code

Development Guidelines

• Keep Servers Updated: Maintain MCP servers with the latest security patches. MCP is a relatively new protocol
• Developer Oversight: Use MCP capabilities as development accelerators while maintaining human oversight and expertise in critical decisions
• Documentation: Maintain clear documentation of MCP server configurations and any customizations

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