"A vector database for efficient similarity search and AI applications" matching MCP servers:

Enables stateless similarity search over pre-computed vector corpora using NMI and cosine fusion with entropy-calibrated weighting. Provides tools for ranking, scoring, outlier detection, and alpha calibration for AI agents.

Combines Neo4j graph database with vector search using OpenAI embeddings for intelligent semantic search across knowledge graphs.

50 tools and 400 functions for working with Excel/.xlsx spreadsheets — read/write, recalculate formulas, diff, repair broken references, and audit. Built for AI agents.

Provides AI assistants with a standardized interface to interact with the Todo for AI task management system. It enables users to retrieve project tasks, create new entries, and submit completion feedback through natural language.

Token-efficient GitLab MCP server that delivers 167 tools through 3 meta-tools with progressive disclosure, field projection, server-side file trimming, and keyset pagination for agent context budgets.

Enables AI assistants to interact with Azure DevOps through an MCP server with 34 tools for PRs, work items, repos, and branches, optimized for token efficiency and agent-first workflows.

Provides agent-friendly web search via MCP with zero-config free engines, region-aware failover, freshness filtering, and a one-call research tool that searches, fetches, and summarizes pages.

A Model Context Protocol server that provides web search capabilities using Google Custom Search API and webpage content extraction functionality.

Enables AI agents to search, watch, summarize, clip, and extract transcripts from YouTube videos, all without needing an API key or leaving the chat.

Provides intelligent search aggregation for AI agents, automatically detecting intent and returning structured results with links to platforms for various categories like movies, books, products, courses, and more.

Enables AI agents to semantically search GitHub repository documentation by automatically fetching, vectorizing, and indexing content into an Upstash Vector database. It provides a standard MCP interface for agents to retrieve relevant documentation snippets through natural language queries.

A clean architecture backend API that registers and semantically searches data using vector embeddings, acting as an MCP context provider to connect AI models to external data.

Read-only MySQL/MariaDB MCP server for running SELECT queries safely, with automatic read-only enforcement and query limits.

MCP server for the AI Patent Search Generator — 11 tools for patent intelligence: dossier (claims, citations, family, classifications, examiner stats), prosecution (USPTO file wrappers), oa_analyze (AI Office Action analysis), search/query (Google Patents multi-strategy), similar, citations, family, examiner, cpc, balance. Install: npx -y patent-search-mcp-server

Enables AI agents to format SQL, explain queries in plain English, analyze schemas, build queries from natural language, and generate migrations, all without requiring a database connection.

Enables SQL database interaction with true server-side result paging, runtime-managed connection profiles, and SSH-bridged access for PostgreSQL.

Enables building and querying vector-based knowledge graphs with node and edge management and semantic search.

Provides AI-powered search and documentation tools using Google Vertex AI or Gemini API with real-time web search grounding, enabling technical queries, code analysis, documentation retrieval, and architecture recommendations to overcome LLM knowledge gaps.

Provides web search and page fetching capabilities using Ollama's API.

提供A股实时行情、历史K线、财务报表、技术指标等全方位数据查询的MCP服务器，支持盘中即时更新及批量查询。