Momento MCP Server

What does “Momento” MCP Server do?

The Momento MCP Server is a Model Context Protocol (MCP) server implementation designed to provide seamless integration between AI assistants and Momento Cache. Acting as a bridge, it enables AI systems to efficiently interact with the Momento caching platform, allowing for operations such as retrieving, setting, and managing cache data. By exposing cache-related operations as MCP tools, it empowers developers to enhance AI-driven workflows with real-time data retrieval, cache management, and resource optimization. This capability is particularly beneficial for tasks like dynamic context injection, fast data lookups, and API integrations, ultimately improving the responsiveness and intelligence of AI applications.

List of Prompts

(No prompt templates are mentioned in the repository or documentation.)

List of Resources

(No explicit MCP Resources are documented or listed in the repository.)

List of Tools

• get
  • Retrieves the cache value stored for a specific key. Returns a hit with the value, a miss if not found, or an error on failure.
• set
  • Sets a value in the cache with an optional time-to-live (TTL). Overwrites existing values for the same key.
• list-caches
  • Lists the names of all caches in your Momento account.
• create-cache
  • Creates a new cache in your Momento account.
• delete-cache
  • Deletes a specified cache from your Momento account.

Use Cases of this MCP Server

• Fast Data Retrieval
  • AI assistants can quickly fetch frequently used data from the cache, reducing latency and API call overhead.
• Dynamic Context Injection
  • Cache values can be dynamically injected into AI prompts, enabling context-aware responses and actions.
• Cache Management Automation
  • Developers can automate cache creation, deletion, and listing directly through MCP-enabled agents, streamlining infrastructure tasks.
• Session or State Management
  • Store and retrieve session data, user preferences, or short-lived state for conversational or interactive applications.
• API Rate Limiting and Response Caching
  • Use the cache as a layer to store API responses, minimizing redundant external calls and improving performance.

How to set it up

Windsurf

(No explicit setup for Windsurf is given in the repository.)

Claude

1. Obtain a Momento API key from the Momento Console.
2. Open your Claude Desktop configuration.
3. Add the Momento MCP Server by inserting the following JSON snippet:

   {
     "mcpServers": {
       "momento": {
         "command": "npx",
         "args": [
           "-y",
           "@gomomento/mcp-momento"
         ],
         "env": {
           "MOMENTO_API_KEY": "your-api-key",
           "MOMENTO_CACHE_NAME": "your-cache-name",
           "DEFAULT_TTL_SECONDS": 60
         }
       }
     }
   }
   

4. Save and restart Claude.
5. Verify the setup by attempting to use the MCP tools within Claude.

Cursor

(No explicit setup for Cursor is given in the repository.)

Cline

(No explicit setup for Cline is given in the repository.)

Securing API Keys

• Always use environment variables to store sensitive information like API keys.

  {
    "env": {
      "MOMENTO_API_KEY": "your-api-key",
      "MOMENTO_CACHE_NAME": "your-cache-name"
    },
    "inputs": {}
  }
  

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "momento": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “momento” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Between the two tables, the Momento MCP Server offers a straightforward and useful set of cache management tools, but lacks advanced MCP features like prompt templates, resources, or sampling support. For developers needing fast cache operations via MCP, it’s practical, but its scope is currently narrow.

MCP Score