"Performing Email-Related Operations in Python" matching MCP servers:

Persistent memory and pattern learning for AI coding assistants via MCP.

A Model Context Protocol server that enables AI agents to query a Graphiti knowledge graph and pgvector document store for evidence-backed responses via hybrid search and RAG.

A simple note-taking MCP server that allows adding notes and summarizing them via a prompt.

A sophisticated AI-powered server providing intelligent, context-aware conversational capabilities with role-based advisors, semantic memory, multi-LLM support, and web browsing.

Enables agents to optimize context-window attention by reordering retrieved documents so critical evidence lands at prompt boundaries, mitigating lost-in-the-middle degradation. It combines BM25 lexical scoring, dense vector similarity, reciprocal rank fusion, knowledge-graph expansion, and recency-decay memory caching, exposed as a zero-dependency MCP stdio server.

Enables agents to run hybrid retrieval, reciprocal rank fusion, knowledge graph expansion, semantic caching, and context reordering so critical evidence is placed at prompt boundaries to reduce lost-in-the-middle degradation.

Enables to run structured QuReDec decision briefs from inside MCP-compatible clients, submitting questions and receiving evidence-backed recommendations with citations.

Exposes the agent's built-in tools, such as file operations, shell commands, web search, memory, skills, and code search, as MCP tools over stdio or HTTP, allowing external MCP clients to invoke them directly.

A Python-based server providing persistent memory management for AI models with SQLite and Markdown dual backend storage. It features full-text search, RAG-enhanced querying, and cross-project knowledge sharing for integration with Claude, Cursor, and Rovo Dev.

A FastAPI server that implements the Model Context Protocol (MCP) using Server-Sent Events (SSE) transport to provide random cat facts on demand or as a continuous stream.

Enables personal knowledge management through Claude Desktop, allowing users to capture thoughts, connect ideas, and reflect on thinking changes via natural conversation.

Local-first MCP server that extracts structured knowledge from markdown notes into SQLite with full-text search, enabling AI coding tools to retrieve relevant context offline at zero cost.

Provides knowledge graph functionality for managing entities, relations, and observations in memory with strict validation rules to maintain data consistency.

MCP server that exposes Open WebUI Knowledge Bases as tools and resources, enabling AI assistants to search and access knowledge bases.

Stores and retrieves personal information fields securely, allowing Claude to access your name, address, email, and professional details without re-typing them.

Enables users to access, search, and get recommendations from AWS documentation through natural language queries. Supports both global AWS documentation and AWS China documentation with tools to fetch pages, search content, and discover related resources.

Connects to Obsidian vaults via the Local REST API plugin, enabling AI-assisted Zettelkasten workflows including creating atomic notes, searching content, managing links and tags, and performing precise content editing operations.

An AI conversation management layer that enables creating chat sessions, persisting message history to GitHub, and performing semantic searches over past interactions. It supports multi-turn threading and context injection to integrate external memory sources into Claude conversations.

Enables AI agents to persist facts with tags, sources, and importance in a local SQLite database, retrieve them through hybrid BM25 plus embedding search weighted by retention decay, link or supersede related entries, and review everything in a read-only visual dashboard showing the memory graph and timeline.

Enables semantic search over markdown files to find related notes by meaning rather than keywords, and automatically detect duplicate content before creating new notes.