"Techniques for enabling agents to retain and utilize information across multiple sessions" matching MCP servers:

Manage a fleet of OPNsense firewalls from an AI agent, inside guardrails it can't drive around. MCP server for central management of OPNsense firewall fleets. 129 tools across devices, config sync, tasks, schedules, templates, backups and remote consoles - destructive actions confirmation-gated, MCP-issued tokens lifetime-bounded, backup and storage secrets excluded from the toolset entirely.

Connect to your TTRPG campaign's repository and database. Instead of retrieving prose, its 48 tools (20 read, 28 write) return typed state: 14 entity schemas (NPCs, factions, locations, sessions, lore), relationship and knowledge-graph queries, wiki blocks, and a narrative-state bundle of open threads and canon facts.

CNN's Fear & Greed index for the US stock market: current score and rating, the seven component indicators, and about a year of daily history. Go standard library only, no API key required.

Provides persistent, searchable memory across AI coding agent and chat history (Claude Code, Codex, Gemini CLI, ChatGPT, and more) via retrieval-augmented generation, enabling semantic and hybrid search to retain context across sessions.

Enables LLMs to interact with embedded devices by reading and writing Segger RTT data through a J-Link debugger.

MCP server that integrates with VictoriaMetrics vmanomaly API to enable AI-assisted anomaly detection, model management, and configuration generation.

MCP server that enables AI assistants to drive systematic literature review workflows, including screening, downloading, converting, and reviewing papers through natural language conversation.

Open source MCP server specializing in easy, fast, and secure tools for Databases.

Interactive PTY sessions for AI agents — local shells, SSH with persistent sessions (ai-tmux daemon for attach/detach), and serial ports. Single Go binary, no tmux dependency.

MCP server that keeps language server sessions warm and routes multiple languages through one process. Agents get persistent cross-file awareness, speculative execution (simulate edits before writing to disk), and 20 skills that encode correct multi-step operations like safe rename, blast-radius analysis, and end-to-end refactoring. Single Go binary, no runtime dependencies.

Failure Memory provides AI coding agents with a shared local memory of failures, enabling them to record, recall, and learn from mistakes across sessions.

MCP server for managing interactive processes, enabling AI agents to start, interact with, and terminate long-running programs like SSH sessions, REPLs, and installers via read/write operations.

Persistent, consensus-validated institutional memory for AI agents. Gives LLMs real memory that survives across sessions - validated through BFT consensus, not just dumped to a file.

Enables read-only search and retrieval of your email across multiple IMAP accounts through a local notmuch index, served over authenticated HTTP for MCP clients like Claude or Codex.

Persistent memory engine for AI coding agents. Single Go binary, zero runtime dependencies, MCP-native. Stores, searches, and deduplicates memories across sessions using embedded SQLite with hybrid FTS + semantic search, memory decay, relation graph, and token-budget context assembly.

Enterprise-grade MCP Server for FHIR-based EMRs. Enables AI agents to read, search, create, and update any FHIR R4 resource across major EHR systems like EPIC, Cerner, and OpenEMR.

MCP server for Meshbrow that gives AI agents full browser automation capabilities with stealth anti-detection, enabling natural language control of browser sessions, data extraction, and multi-browser fleets.

High-performance MCP data plane server for Nextcloud integration, enabling AI agents to read, write, organize, and manage files and CRM assets over WebDAV and OCS APIs.

A self-hosted MCP server that provides capability packs for AI agents to perform browser, desktop, git, and code actions via schema-validated JSON calls, enabling reliable agentic workflows even with small open-weight models.

Enables AI agents to capture, store, and retrieve durable learnings from projects via MCP tools, providing a queryable memory of product and technical lessons across repos.