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Why this server?
This server enables Large Language Models to interact with Elasticsearch clusters, allowing them to manage indices and execute search queries using natural language.
Why this server?
An MCP server implementation that enables AI models to discover, search, and analyze data stored in Typesense collections through tools for querying documents, retrieving specific items, and accessing collection statistics.
Why this server?
Provides a semantic memory layer that integrates LLMs with OpenSearch, enabling storage and retrieval of memories within the OpenSearch engine.
Why this server?
Provides semantic search capability over Obsidian vaults and exposes recent notes as resources to Claude through the MCP protocol.
Why this server?
A server that enables MCP clients like Anthropic Claude App to interact with local Zotero libraries, allowing users to search papers, manage notes, and access research materials through natural language.
Why this server?
A Model Context Protocol server that enables semantic search and RAG over your Apple Notes, allowing AI assistants like Claude to search and reference your notes during conversations.
Why this server?
Enables AI assistants to query and analyze Azure Data Explorer databases through standardized interfaces.
Why this server?
Persistent development memory server that automatically captures and organizes development context, code changes, and user interactions across projects.
Why this server?
A server that enables document searching using Vertex AI with Gemini grounding, improving search results by grounding responses in private data stored in Vertex AI Datastore.
Why this server?
A Python implementation of an MCP server that extracts webpage content, removes ads and non-essential elements, and transforms it into clean, LLM-optimized Markdown.