"How to send emails" matching MCP servers:

Self-improving, verifiable memory for AI coding agents. Learns how you work, stops repeating mistakes, models each project, recalls the right lesson at the right moment. Every memory is signed and tamper-evident. Local-first.

Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.

Enables Claude chats to communicate across machines or sessions via shared encrypted rooms, with tools to create, join, send, read, wait, search, and delete room messages.

Provides full access to Hookmark on macOS, enabling users to create and manage bidirectional links between files, URLs, emails, and notes. It supports searching bookmarks, connecting items, and retrieving links from active applications through natural language.

Enables users to visualize the impact of edits in internal documents and emails by structuring content as a graph and tracing dependency chains.

A redaction engine and MCP server that scrubs sensitive identifiers (AWS keys, IPs, emails, etc.) from AI agent traces, enabling safe storage and sharing of war stories via submit, search, and retrieve tools.

The most complete Signal MCP server and CLI.

A codebase ontology and a cited wiki, both kept as Markdown in one folder on your disk. Typed graph answers — what a change affects, how two concepts connect, which wiki lines lack a citation — and every write lands as a Git diff a person reviews.

Local-first personal memory for AI agents. Captures your Feishu chats, emails, meetings, and AI coding sessions into a private memory graph, and serves it to any agent over MCP.

Provides OpenCode with adaptive long-term memory by storing preferences, lessons, and usage history locally in SQLite, enabling AI to remember and adapt to users through context-based learning.

Turn Obsidian notes into agent-ready context, preview-only plans, and safe repo handoffs.

Gives Claude persistent, local context about how you work, your weekly agenda, and shipped work, with automatic work logging and git integration—all stored privately on your machine.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Persistent, local memory for AI coding agents that learns how you work, not just what you said. Supports Claude Code, Codex CLI, Cursor, and any MCP client.

Enables AI agents to manage persistent context, send signed messages, sync with peers, and share files through plain-file-based tools.

Enables a small local LLM to build and maintain a project map by offloading event ID/date generation, batching, synchronization, recovery, and finalization to the server, so the model only decides what to read and how to classify.

Serves portable behavioral identity models via MCP. Extracts how a person thinks, communicates, and makes decisions from text, then provides always-on identity context, semantic fact retrieval, keyword search, and provenance tracing through 4 tools and 1 resource.

Connects your personal Althea AI assistant to MCP-compatible tools like Codex and Claude, allowing you to send messages and retrieve conversation history.

Local-first semantic memory server with project indexing for AI assistants. It enables AI assistants to store, retrieve, and search memories and project code using embeddings and vector search.

🧠 High-performance persistent memory system for Model Context Protocol (MCP) powered by libSQL. Features vector search, semantic knowledge storage, and efficient relationship management - perfect for AI agents and knowledge graph applications.