A server for Zep, a long-term memory store for AI applications, requiring a ZEP_API_KEY for access.
This repository contains the MCP (Model Context Protocol) server for the ZepAi module. The server is configured to run using a specific command and environment variables as detailed below.
Below is an example configuration:
{
"mcpServers": {
"zepai": {
"command": "uv",
"args": [
"run",
"--directory",
"<path_to_the_mcp_directory>",
"python",
"-m",
"mcp_zepai"
],
"cwd": "<path_to_the_mcp_directory>",
"env": {
"ZEP_API_KEY": "<your_api_key_here>"
}
}
}
}
uv
.ZEP_API_KEY
.To run the MCP ZepAi server, ensure that the configuration file is correctly set up and execute the following command:
/opt/homebrew/bin/uv run --directory <path_to_mcp_directory> python -m mcp_zepai
Ensure that the ZEP_API_KEY
environment variable is set with a valid API key.
Integrates LLM applications with documentation sources using the Model Context Protocol.
Provides structured data for shadcn/ui components, including descriptions, installation instructions, usage examples, and props.
Performs complementary code analysis by combining Claude Code and Google's Gemini AI.
An MCP server for interacting with the Tatara blockchain ecosystem. Requires configuration for the Tatara RPC endpoint and a wallet private key.
A comprehensive MCP server for tooling interactions(40+) and resource accessibility(60+) plus many useful prompts to interact with Algorand Blockchain.
An implementation of the Model Context Protocol (MCP) for communication between AI models and external tools, featuring server and client examples in Python and Spring Boot.
A lightweight, LLM-agnostic RESTful proxy that unifies multiple MCP servers under a single API.
An example of deploying a remote MCP server on Cloudflare Workers without authentication.
Remote, no-auth MCP server providing AI-powered codebase context and answers
A local MCP server implementing Retrieval-Augmented Generation (RAG) with sentence window retrieval and support for multiple file types.