"Creating a Memory Database for LLM Chat Conversations" matching MCP servers:

Wraps Tool and ToolPackage instances from the llm-chat ecosystem behind the Model Context Protocol, enabling stdio and Streamable HTTP transports for MCP servers.

Enables agents to inspect configured LLM models and send chat requests to them, with support for parameter overrides, file-based prompts, and output to files.

MCP server that enables persistent, hybrid, local memory for LLM agents, with vector + BM25 search, knowledge graph, and policy-driven retention, providing token-budgeted context injection for AI assistants.

Enables MCP-compatible AIs to read a user's local three-layer memory of their own typed chat messages, including raw text, conversation summaries, and theme indexes, so responses can be grounded in the user's past decisions and preferences.

MCP server providing 1:1 tool parity with Arcade.dev's HubSpot Conversations API toolkit, enabling reading and sending conversation messages, managing threads, inboxes, channels, and custom channels from any MCP client.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

Enables AI assistants to list and read existing ChatGPT conversations and send messages into them, retrieving the assistant's reply through ChatGPT Desktop's native bridge. It works without cookies, UI automation, or an OpenAI API key.

A local MCP server for durable, named Kiro CLI conversations over ACP, enabling asynchronous runs with session management, model querying, and bounded wait delegation.

Enables AI applications to run SQL queries and retrieve results from Oracle Database.

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 maintain persistent, searchable two-layer memory with 37 tools, hybrid search, knowledge graphs, and enterprise features like authentication and backups.

Enables AI assistants across platforms to share memory, recall facts, preferences, and conversation history, creating a unified cognitive system.

Enables to perform mathematical calculations, retrieve weather information, and manage files through a standardized MCP interface, showcasing best practices for building MCP servers.

A read-only MCP server that enables AI agents to explore database schemas and execute safe queries on PostgreSQL and MySQL.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Enables remote control of the Cursor AI agent via chat platforms like GitHub Issues or Telegram, allowing users to start sessions and receive summaries and prompts in a chat thread.

Read-only MySQL/MariaDB MCP server for running SELECT queries safely, with automatic read-only enforcement and query limits.

Allows AI assistants to access and analyze your Cursor conversation history, enabling personalized coding assistance based on your actual development patterns.

Visualizes conversations as structured hierarchical mind maps by injecting a prompt that instructs the LLM to generate a JSON mind map — no external APIs required.

Blind multi-model councils with anonymous LLM seats and direct 1:1 chat as a local MCP server. Enables asking one question to get independent answers from multiple model CLIs, then blind scoring and reveal.