"How to execute EKS (Elastic Kubernetes Service) commands" matching MCP servers:

Connects to Elasticsearch clusters through the Model Context Protocol, enabling natural language querying and management of Elasticsearch data. Provides tools to search indices, list available indices, and retrieve index mappings.

An Elasticsearch-based AI memory system optimized for Chinese that enables persistent knowledge storage and complex entity relationship management via the Model Context Protocol. It features advanced semantic search using the IK analyzer and supports multi-zone memory isolation for specialized knowledge graphs.

Connects LLMs to Elasticsearch with a Semantic-to-Lexical layer that translates technical field names into business knowledge, enabling autonomous querying without hardcoded domain logic.

Self-hosted service providing unified memory search and write operations across file-based memory, vector embeddings, and the Graphiti temporal knowledge graph, accessible via HTTP API or MCP stdio transport.

A production-minded RAG service for MCP that answers questions over your documents with hybrid retrieval, PII redaction, and source citations, packaged for Docker/Kubernetes.

Provides self-hosted, versioned project memory with full-text and vector search, enabling search, retrieval, upsertion, and history management of project entries via MCP tools.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

Provides semantic memory and persistent storage for Claude, leveraging ChromaDB and sentence transformers for enhanced search and retrieval capabilities.

Enables AI assistants to search internal knowledge bases, create Confluence articles, file Jira tickets, and look up Okta users, with guardrails and streaming support.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

Provides hybrid retrieval (dense + BM25 + RRF) with collection-based isolation and document ingestion for private knowledge access via MCP.

This MCP server provides semantic document search and retrieval, enabling AI assistants to search documents, search categories, and retrieve category hierarchies using the Model Context Protocol.

A Cloudflare Worker that transforms Cloudflare AI Search (AutoRAG) instances into an MCP server for querying documentation. It enables AI models to search and retrieve relevant information from custom document sets stored in R2 buckets.

A clean architecture backend API that registers and semantically searches data using vector embeddings, acting as an MCP context provider to connect AI models to external data.

An MCP (Multi-Agent Conversation Protocol) Server that enables interaction with Adyen's Checkout Utility Service API through natural language, auto-generated using AG2's MCP builder.

Enables Claude Desktop and other AI tools to interact directly with Figma, allowing for powerful AI-assisted design capabilities through natural language commands.

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

This project builds a read-only MCP server. For full read, write, update, delete, and action capabilities and a simplified setup, check out our free CData MCP Server for Jira Service Management (beta): https://www.cdata.com/download/download.aspx?sku=GKZK-V&type=beta

MCP server that ingests PDF documents into pgvector for semantic search and RAG pipelines. It handles extraction, chunking, local embeddings, and storage, enabling agents to make PDFs searchable via natural language.