"Retrieving information from document directory with search and memory capabilities" matching MCP servers:

An MCP server that exposes any REST API to LLMs through runtime discovery, providing tools to browse endpoints, fetch schemas, and apply business rules without hardcoding or schema duplication.

A personal MCP bridge that lets ChatGPT drive a local Codex CLI, translating instructions into Codex app-server threads and returning compact summaries of what Codex did.

Enables AI agents to discover, register, and rate services in a decentralized agent-to-agent directory.

MCP Server for agent and service discovery — register, search, rate, and manage AI agents and services in a local directory.

Universal search engine for AI agents. Discover products, services, and businesses across every category. 10 MCP tools, zero LLM calls, millisecond responses.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Enables AI-powered web searches using Google's Gemini 2.5 models with Google Search Grounding. Supports multiple Gemini models (Flash, Flash-Lite, Pro) with configurable thinking budget and web search capabilities.

An advanced contextual memory server for agent orchestration providing chronological history, semantic vector search via Redis Stack, and temporary scratchpad storage. It also enables context compression through history rollups and facilitates inter-agent communication using a Redis-based Pub/Sub system.

Enables AI agents to perform web search, extraction, and research across multiple provider backends with persistent multi-key rotation and auditable routing. Supports MCP, CLI, and Agent Skill entry points.

Model Context Protocol (MCP) memory server built with Azure Functions and TypeScript, providing persistent knowledge graph storage for your preferred MCP client. Inspired by and forked from @modelcontextprotocol/server-memory

A shared memory and coordination server for multiple AI coding agents, built on the Model Context Protocol (MCP).

Enables parallel research across multiple cheap AI worker models, splitting a question into sub-questions and synthesizing one sourced answer with references.

Provides MCP tools for persistent, cross-session memory via Cloudflare Agent Memory, enabling agents to store, recall, ingest, summarize, and manage memory namespaces.

Provides a standardized protocol for tool invocation, enabling an AI system to search the web, retrieve information, and provide relevant answers through integration with LangChain, RAG, and Ollama.

Enables AI agents to access user and message data through MCP resources, providing REST API integration for user management with paginated lists and thread tracking.

Enables AI tools and agents to share a persistent memory space, so a decision, task, or encrypted secret saved in one client can be searched, recalled, and handed off from another. It also tracks goals day by day, moves unfinished work between sessions, and keeps exports, trash recovery, and per-account isolation under the user's control.

A lightweight MCP server that records and reuses prompt gaps — essential context missing from initial instructions — so your AI assistant learns from every session using a plain JSONL file tracked by Git.

A meta-MCP server that aggregates authoritative web search and browser research tools, providing a unified interface with a cost-efficient fetch ladder for web research.

An MCP server that exposes an Agno agent with Google Gemini reasoning and Serper web search, enabling MCP-compatible clients like Claude Desktop to perform agent-driven web searches and receive synthesized answers.

A multi-engine web search MCP server that integrates with coding agents, offering parallel search, page fetching, X/Twitter search, deep research, and configurable free/API layers.

An MCP server that enables web search capabilities using OpenAI's o3 model, allowing AI assistants to perform text-based web searches and return AI-powered results.

A Model Context Protocol Server search realtime flight detail with multiple fligh carrier, price , stop , time duration for any given date using simple prompt

Exposes SearXNG web search and query autocomplete to AI clients, returning ranked results, instant answers, and suggestions with filters for category, language, time range, engines, page, and safe search. It is distributed via npx and requires a self-hosted SearXNG instance with the JSON format enabled.

Provides a centralized MCP-based system for managing and accessing multi-project memory banks remotely, with features like project isolation, file structure validation, and type-safe operations.

A persistent memory implementation using a local knowledge graph that lets Claude remember information about users across conversations.

Enables MCP-compatible agents to recall approved durable memory and propose new or replacement memories for review, without granting direct write or administrative access.

Enables AI agents to persist and share durable memory — appending session logs with checkpoints and handoffs, claiming and completing tasks, and recording decision records — all stored as plain Markdown and JSONL files in a git repository. Everything written is full-text searchable, and the server speaks MCP so MCP-aware clients can plug in directly with zero vendor lock-in.

Enables Claude Code sessions to persist and share context by saving, searching, organizing, checkpointing, and restoring work history, decisions, and progress across sessions and parallel assistants.

Enables free-first web search in English and Chinese with no API key required, returning compact multi-source evidence while keeping provider failures, stop reasons, and budget limits visible. Optional paid providers are used only when routing policy and credentials allow.

Enables web search for the pi coding agent using z.ai GLM native search with a DuckDuckGo fallback.