#retrieval

An MCP server implementation for retrieving information from the AWS Knowledge Base using the Bedrock Agent Runtime.

ContextBridge (CB) is a local-first retrieval layer that sits between your codebase and your AI coding agent. Instead of pasting raw source files into context or letting the AI guess which files matter, CB indexes your codebase's real structure (via Graphify) and returns a compact, ranked result — owner file, related files, key symbols, and dependency chains — grounded in your actual code. A typical response is ~4–8 KB instead of the 100+ KB of raw source an AI would otherwise need to read to answer the same question — roughly a 96% cut in input tokens sent to your cloud AI, with zero hallucinated file paths or method names.

Local-first code retrieval for AI agents — cuts codebase context from tens of thousands of tokens to a few hundred, with zero hallucinated file paths.

An MCP server that resolves under-specified, half-remembered queries for AI agents into verified answers — or an honest decline. Built on the principle that it never returns a confident-wrong: when it can't resolve with confidence, it shortlists honestly or declines rather than guessing. Free remote endpoint, streamable HTTP transport.

AccInt is a local Work Model and MCP server for persistent scored agent memory, retrieval, commitments, browser/runtime observations, and outcome-based learning across coding agents.

Remote MCP server for geographic data — earthquakes, tsunamis, volcanoes, hurricanes, floods, tornadoes, FX rates, UN SDG, World Factbook, WorldPop. Free discovery + free packs; earthquakes & tsunamis paid via x402 on Base USDC. Start with get_catalog, then get_pack.

Forge is Voxell's hosted text-embedding API. This MCP server exposes two tools — embed (turn text into vectors) and list_models — so any MCP-compatible agent can generate embeddings for semantic search, RAG, clustering, and dedup. Three tiers let you dial quality against cost: turbo (1024d, fast and cheap), pro (2560d), and ultra (4096d — Qwen3-Embedding-8B, ~75+ average task score on MTEB and currently #4 on MTEB English; the models above it are research-only, so it's the top usable model). Matryoshka dimensions let you truncate to fewer dims — re-normalized — for cheaper vectors. Requires a FORGE_API_KEY (free tier at dash.voxell.ai). Embeddings only — no data is stored.

The open retrieval layer for AI coding agents. Indexes code, docs, legal, research, data — 15 parsers, FTS5 + semantic search, knowledge graph. Serves surgical context via MCP. Open source, local, free. Index everything in your project — code, docs, PDFs, legal texts, research, data — and your agent finds the right context instantly. No hallucinations. No cloud. No API costs.

The Interplanetary File System (IPFS) MCP toolkit gives AI Agents and automated workflows the ability to utilize IPFS as a backend storage medium. Upload, retrieve, pin, and manage encrypted files on IPFS via a simple API accessible as MCP tools.

Search, retrieve, and verify structured entity data for businesses on the Nordax AI Entity Network. Provides AI systems and agents with machine-readable business profiles, JSON-LD structured data, and entity verification status for over 360,000 businesses.

PinRAG is a retrieval-augmented generation (RAG) MCP server built with LangChain and Chroma. Index PDFs, plain text, Discord exports, YouTube transcripts, and GitHub repositories, then ask questions in Cursor, VS Code (Copilot), or any MCP-capable client. Answers include citations (pages, timestamps, paths). Install via PyPI (`pipx`, `uv tool`, or `uvx --from pinrag pinrag-mcp`). Configure API keys in your editor’s MCP `env` block.

Cognitive memory system for AI agents with 129 MCP tools. Persistent hierarchical memory, emotional recall, knowledge graphs, spreading activation, hybrid retrieval (BM25+RRF), metacognition, goal tracking, spaced repetition, dream consolidation, and more.

MCP persistent memory service that gives AI assistants long-term memory across sessions and projects. Powered by Qdrant vector search + Ollama/Gemini embedding with hybrid retrieval (vector + BM25). Features include: semantic memory save/search/forget, content deduplication via SHA-256, automatic sensitive data redaction, dual-shell architecture (MCP stdio + HTTP REST), Web Admin Panel with analytics, multi-user auth & API key management, and audit logging.

MCP server for Voyage AI embeddings, reranking, and MongoDB Atlas Vector Search. Provides 11 tools for semantic search, document ingestion, cost estimation, and model exploration. Full RAG pipeline: chunk, embed, vector search, and rerank from any MCP client.

用于图片搜索,来搜寻你想要的图片吧

The Jotform MCP server provides programmatic access to forms and submissions via natural language interfaces, compatible with popular AI assistants including Cursor and VS Code extensions. Implementation follows the open Model Context Protocol specification, ensuring interoperability with any MCP-compatible client. The service is actively being optimized based on real-world usage patterns.This MCP is actively maintained and more tools will come soon.

Retrieves news articles from google alerts service on specific topics.

根据提示词搜索图片

**Context Keeper** 基于LLM驱动的智能上下文记忆管理系统，专为AI Agent提供企业级记忆能力。 **核心创新**：独创"宽召回+精排序"架构，通过两阶段LLM协作实现多维度检索融合（向量+时间线+知识图谱），准确率从传统RAG的45%提升至**75%+**，召回率达**80%+**。 **技术亮点**：完整实现MCP协议（HTTP/WebSocket/SSE三种传输），四维统一上下文模型（Project/Topic/Conversation/Code），支持短期/长期记忆智能转换。 **性能表现**：10000+ QPS并发能力，P95延迟<500ms，用户/工作空间完全隔离。 **开源生态**：MIT许可，Go语言实现，深度集成Cursor/VSCode IDE。让AI助手真正拥有持续记忆能力，告别重复解释，开启智能协作新时代。

Transform your AI agents into Apple development experts! Apple RAG MCP gives you instant access to official Swift docs, design guidelines, and comprehensive Apple platform knowledge through cutting-edge RAG technology. With professional AI reranking and hybrid search across iOS, macOS, watchOS, tvOS, and visionOS documentation plus Apple Developer YouTube content, you'll get precise, contextual answers every time. Compatible with Cursor, Claude Desktop, and all MCP tools - start building smarter Apple apps today!

Advanced MCP server for comprehensive web research, content discovery, and trends analysis. Features multi-engine search, intelligent content extraction, website traversal, and real-time data streaming.

Scaffold is a specialized RAG system that transforms entire codebase into a living knowledge graph, capturing the deep structural relationships between files, classes, and functions. This provides AI agents with the crucial context to accurately understand, maintain, and reason about complex software, moving far beyond simple text retrieval.

B 站视频评论获取工具.

MCP Server to retrieve relevant information from HOA documents

MCP Server using Amazon Q Business Cross App index

A Model Context Protocol (MCP) server for agentic retrieval of financial data. This server leverages YFinance to provide a simple and efficient way to access historical stock prices, dividends, stock splits, company information, and other financial metrics. This MCP server is designed to be used with various LLM applications that support the Model Context Protocol, such as Claude Desktop and Cursor. It allows users to retrieve financial data in a structured way, making it easy to integrate into AI applications.

A Model Context Protocol (MCP) server for agentic retrieval of financial data from Yahoo Finance. This server leverages YFinance to provide a simple and efficient way to access historical stock prices, dividends, stock splits, company information, and other financial metrics. This MCP server is designed to be used with various LLM applications that support the Model Context Protocol, such as Claude Desktop and Cursor. It allows users to retrieve financial data in a structured way, making it easy to integrate into AI applications.

A Model Context Protocol (MCP) server for agentic retrieval and semantic search over unstructured and structured data using Milvus, a high-performance vector database. This server enables large language model (LLM) applications to efficiently index, store, and retrieve vector embeddings derived from diverse data sources—such as documents, images, metadata, or logs. By leveraging Milvus, the MCP server supports similarity search and contextual retrieval, enabling intelligent access to relevant information based on natural language queries. Designed for integration with MCP-compatible clients, this solution provides a scalable, low-latency foundation for building AI-powered applications with contextual awareness and retrieval-augmented generation (RAG) capabilities.

A Model Context Protocol (MCP) server for agentic retrieval of financial data from Yahoo Finance. This server leverages YFinance to provide a simple and efficient way to access historical stock prices, dividends, stock splits, company information, and other financial metrics. This MCP server is designed to be used with various LLM applications that support the Model Context Protocol, such as Claude Desktop and Cursor. It allows users to retrieve financial data in a structured way, making it easy to integrate into AI applications.

A Model Context Protocol (MCP) compliant server designed for comprehensive web research. It uses Tavily's Search and Crawl APIs to gather detailed information on a given topic, then structures this data in a format perfect for LLMs to create high-quality markdown documents.

Self-hosted MCP Japanese text indexing & search—chunking+embeddings with BM25×vector rerank

A Model Context Protocol (MCP) compliant server designed for comprehensive web research. It uses Tavily's Search and Crawl APIs to gather detailed information on a given topic, then structures this data in a format perfect for LLMs to create high-quality markdown documents.

A Model Context Protocol (MCP) compliant server designed for comprehensive web research. It uses Tavily's Search and Crawl APIs to gather detailed information on a given topic, then structures this data in a format perfect for LLMs to create high-quality markdown documents.

A Model Control Protocol (MCP) server for exploring the GitHub GraphQL schema and executing optimized queries. Provides AI assistants with efficient GitHub data retrieval capabilities through GraphQL.

OAuth2 Authorization Server with Spring Boot 3 and Java 24

MCP server that provides tools for metadata retrieval from SharePoint and entity retrieval from a Neo4j knowledge graph using semantic similarity search.

Final v4 release of Vibe Coder MCP Server with Automatic Contextual Retrieval System (ACRS) tools

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A RAG-ready MCP server for semantic PDF search with OCR, FAISS, and transformers—plug into any MCP client and retrieve intelligent answers within your MCP client.

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A suite of Model Context Protocol (MCP) servers designed to enhance AI agent capabilities. Provides tools for media search/understanding (images, video), web information retrieval, PDF generation, and PowerPoint presentation creation, enabling agents to interact with diverse data formats and external resources

🦀 Prevents outdated Rust code suggestions from AI assistants. This MCP server fetches current crate docs, uses embeddings/LLMs, and provides accurate context via a tool call.

A code repository indexing tool to supercharge your LLM experience.

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A Model Context Protocol (MCP) server

A memo tool based on MCP protocol that helps developers quickly save and retrieve text information without interrupting their workflow.

One simply siphons the excess thoughts from one's mind, pours them into the basin, and examines them at one's leisure. It becomes easier to spot patterns and links, you understand, when they are in this form.

mcp test

MCP server for querying Dutch postal codes

A Model Context Protocol (MCP) server for retrieving data from Prometheus databases.

An MCP Server to get data from EDGAR

Typescript implementation of MCP server for Valyu Network API (

Model Context Protocol (MCP) Server for National Park Services data

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MCP Deep Research Server using Gemini creating a Research AI Agent

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