"A server for business purposes" matching MCP servers:

Enables AI agents to provide e-commerce customer service with product search, RAG-based FAQ answers, human handoff, and automated security auditing with a review workflow.

An MCP server exposing internal business operations as tools — task management (create, list, update status) and RAG-style semantic search over an internal knowledge base (leave, expense, and onboarding policies) that any MCP-compatible AI agent can call directly for grounded, non-hallucinated answers.

Provides versioned, structured memory for AI agents, allowing them to store facts, detect conflicts, and track knowledge history via a hosted SaaS platform. It enables efficient hierarchical information retrieval and semantic search while keeping token usage constant as memory scales.

An Operit-compatible adapter of the Exa MCP server, enabling web search, code search, and company research capabilities in AI assistants. It fixes MCP handshake compatibility issues, allowing tools like web_search_exa and web_fetch_exa to load and run reliably.

Simple, fast, long-term memory for your AI chatbots, agents, and MCP tools. Give your LLM a persistent memory using a local SQLite file. No complex database servers, no heavy cloud setup, and zero C++ compilation errors. Works everywhere.

Stop paying for your agent to rediscover what other agents already figured out. Prior is a shared knowledge base where agents exchange proven solutions — one search can save 10 minutes of trial-and-error and thousands of tokens. Your Sonnet gets access to solutions that Opus spent 20 tool calls discovering. Search is free with feedback, and contributing earns credits.

Query SQL databases (SQLite, PostgreSQL, BigQuery, Databricks) in natural language through a business semantic layer — glossary, metrics, and a data dictionary grounded against your real schema. Read-only by default, with an embedded SQLite + sqlite-vec metadata store and no external infra required.

Enables AI assistants to search a curated database of 200+ AI tool cards with pricing, platforms, use cases, and source links, so users can ask which tool to use for a task and get grounded recommendations.

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.

Exposes Microsoft BCQuality repository (Business Central development best practices) as structured tools for AI agents, enabling rule discovery, search, and application via MCP protocol.

A persistent, project-scoped memory layer for AI agents, supporting hybrid retrieval (vector, keyword, and tag matching) and sharing across different MCP clients like Claude Code, Qoder, or Cursor.

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

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

Provides a compiled knowledge substrate, extracting atomic claims from an append-only capture log and querying a bitemporal claim graph over MCP. Read-only by default, with opt-in writes for trusted sources.

Enables AI coding agents to intelligently index and search codebases with sub-20ms retrieval, 8x memory compression, and cross-encoder reranking via MCP stdio.

A local MCP server that indexes files in a directory using Gemini Embedding 2, enabling AI agents to perform semantic search over local documents.

Implementation of Model Context Protocol (MCP) server that provides tools for accessing Google Cloud's Vertex AI Gemini models, supporting features like web search grounding and direct knowledge answering for coding assistance and general queries.

MCP server for a production-shaped service-business agent, enabling grounded knowledge search, intake collection, and human escalation with deterministic safety boundaries.

A TypeScript-based server to interact with ArangoDB using the Model Context Protocol, enabling database operations and integration with tools like Claude and VSCode extensions for streamlined data management.

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.

A Model Context Protocol server that provides AI agents with read-only access to Rossum API resources (queues, schemas, hooks, workspaces) for analysis purposes.

Integrates with Get笔记 (GetBiji) API to enable AI-powered knowledge base search and retrieval. Provides tools for enhanced semantic search, raw content recall, and rate limit monitoring.

Enables natural language interaction with MySQL Sakila database through intent-based tools for movie search, customer management, rental operations, and business analytics without exposing database schema.

Enables AI assistants to intelligently select and switch between different AI models (OpenAI, Anthropic, etc.) within the same conversation based on task requirements. Provides a unified interface for accessing multiple AI providers through a single MCP tool.

Provides read-only hybrid RAG search and discovery over a local-first AI knowledge corpus, enabling semantic and keyword search, browse, digest, and status tools.

MCP for Azure DevOps Boards is a MCP server that lets your favourite AI browse, query and update Azure DevOps work items as if it were a project manager. Written in Rust and optimized for tokens usagem, It runs via stdio or HTTP mode and uses standard Azure authentication with az login.

A custom MCP tool that integrates Perplexity AI's API with Claude Desktop, allowing Claude to perform web-based research and provide answers with citations.

Provides an MCP protocol interface for interacting with Elasticsearch 7.x databases, supporting comprehensive search functionality including aggregations, highlighting, and sorting.

A Python server implementing the Model Context Protocol to provide customizable prompt templates, resources, and tools that enhance LLM interactions in the continue.dev environment.

Enables AI agents to persist, search, and evolve knowledge through a Markdown vault with a typed knowledge graph and MCP interface.