"jQuery" matching MCP servers:

A Model Context Protocol (MCP) server that provides read-only TDengine database queries for AI assistants, allowing users to execute queries, explore database structures, and investigate data directly from AI-powered tools.

Advanced content gap analysis using Query Decomposition and Keyword Fan-Out (Google's patented methodology). Tells you exactly what user queries your content covers - and what it misses. Built on academic research because I needed to understand how AI search engines actually evaluate content.

Provides secure, read-only PostgreSQL database access via MCP tools like query_inventory and get_top_sales. Blocks dangerous SQL commands while allowing AI to execute controlled SELECT queries.

Enables decomposition of complex research queries into multi-hop sub-queries and synthesis DAGs, with scientific consensus analysis, citation credibility verification, and deterministic JSON outputs for MCP-compliant clients.

Provides hybrid search over local markdown knowledge bases using BM25 keyword search and vector semantic search. Enables indexing, querying, and retrieving markdown documentation with dual-mode support for local and remote agents.

Enables semantic search and retrieval over local Markdown/MDX documentation using Node.js-based embeddings. Supports multi-language documentation with offline vector indexing and MCP tool exposure for AI assistants.

Enables AI-powered natural language stock market queries with real-time data from Yahoo Finance and CSV fallback. Uses Google Gemini to interpret queries and automatically fetch stock prices, comparisons, and market summaries.

Enables querying and analyzing user, product, and order data with filtering capabilities and real-time statistics. Supports WebSocket connections to XiaoZhi AI platform with automatic reconnection.

A RAG-based MCP server for natural language querying of Brazilian healthcare manuals (SIH/SUS, SIA/SUS) and official ordinances. It provides 16 tools for semantic search, regulatory critique analysis, and retrieving data from SIGTAP and CNES.

A Model Context Protocol (MCP) server that converts natural language queries into SQL statements, allowing users to query MySQL databases using conversational language instead of writing SQL code.

Enables decomposition of complex, multi-faceted research prompts into executable query plans and synthesis DAGs, with support for scientific evidence analysis and consensus calculation.

Enables natural language search through personal video collections, returning AI-generated descriptions and direct URLs to relevant 30-second video clips.

Enables querying a hybrid-retrieval RAG pipeline (dense + BM25) over ingested PDF documents, returning answers generated by Gemini.

Enables agents to perform controlled enterprise data queries through semantic intent, with runtime validation of statistics, filters, granularity, permissions, and physical bindings. Exposes tools like semantic_query for safe, fail-closed access to data horizons and capabilities.