"Integrating Web3 Wallet Functionality in AI Chat Applications" matching MCP servers:

A basic Model Context Protocol server implementation that demonstrates core functionality including tools and resources for AI chat applications.

A conversational chat interface that uses RAG over a synthetic test corpus and a domain-knowledge corpus, exposing retrieval via MCP.

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.

An MCP server that enables analyzing and querying GitHub repositories through the GitHub Chat API, allowing users to index repositories and ask questions about their code, architecture and tech stack.

Neo4j 기반 챗봇 MCP 서버로, 도메인 특화 tool과 Text2Cypher 방식을 조합해 안전하고 정확한 지식그래프 질의응답을 제공합니다.

Enables interactive AI chat with document retrieval and command-based tool integrations via the Model Control Protocol.

Enables AI coding agents to persistently store and retrieve conversation history with hybrid semantic and keyword search, cross-encoder reranking, session filtering, and archiving through MCP tools.

An MCP server that turns the Hugging Face Hub into a conversational model picker with a tool-use eval harness to prove the agent actually uses it correctly.

Google Chat MCP server that lets AI assistants like Claude and Cursor participate directly in team conversations - search messages, help teammates, share files, and coordinate across chat platforms.

All In One Model Context Protocol: qdrant, google suite (gmail, calendar, ...), jira, gitlab, CLI, ...

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Provides a read-only interface to audit and continue coding agent sessions by extracting plans, intents, and edit authorship from history across multiple agents (Claude, Codex, OpenCode, Antigravity, Pi) via MCP, CLI, and Python SDK.

Ingests pdf and epub documents for LLM-retrieval (RAG)

A read-only MCP server that provides document awareness for agents by parsing local files into structured profiles, blocks, chunks, and search results, enabling agents to understand and cite document content without dealing with raw file formats.

Enables interactive chat with AI models via Anthropic API, supporting document retrieval and extensible command-based tool integrations.

An MCP server with RAG capabilities that provides AI clients secure access to local notes, documents, web pages, and task management tools for developer operational tasks. Features file operations, web scraping with content cleaning, and personal knowledge corpus search functionality.

A simple AI development tool that helps users interact with AI through natural language commands, offering 29 tools across thinking, memory, browser, code quality, planning, and time management capabilities.

Interact with WebScraping.AI API for web data extraction and scraping

A lightweight RAG system that provides an MCP server for searching and interacting with vector-based knowledge bases. It enables users to perform retrieval-augmented generation and search across Qdrant collections through a standardized interface.

Open-source AI memory layer for LLM agents. Importance scoring, temporal decay, hierarchical memory (facts, summaries, themes), YMYL prioritization, and active retrieval with contradiction detection. Supports OpenAI, Anthropic, Ollama. Local-first with SQLite + FAISS.