"A tool for web browsing" matching MCP servers:

基于Model Context Protocol (MCP) 的A股实时行情查询服务器，支持查询A股实时价格、历史K线数据、财务信息及市场概况。

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.

Lets an AI agent pull the user's own Douyin creator-backend work data — plays, likes, comments, shares, saves, 5-second completion rate and more — into a locally accumulating CSV plus a daily overview, with tools for status checks, fetching, merging, and retrospective analysis. Access is read-only to the user's own account, needs no third-party dependencies, and keeps all data on the local machine.

Enables clients to interact with PipesHub data and functionality through MCP, including chat, search, record retrieval, directory browsing, sources, and agents.

Web extraction MCP server for AI agents. Extract structured data from any URL with built-in Cloudflare bypass, JavaScript rendering, and intelligent parsing. Returns clean markdown or JSON.

MCP server that diagnoses Apache Spark job failures and optimizes performance using stack-trace analysis and LLM providers, supporting EMR and local sources.

Answers "sales dropped since last week — where?" by comparing a target period against a weekday-adjusted baseline and localizing which attribute combinations (e.g. channel=web, or Tuesday nights) explain the shift. Runs fully offline on your own CSV — no API key, no ML training, read-only.

Enables an AI assistant to answer customer-success questions by synthesizing simulated billing, support ticket, and product usage data into composite churn-risk scores, bands, and plain-language explanations for fictional accounts. It exposes raw-data tools, a synthesis tool, and a methodology resource so users can list at-risk accounts or get a per-account health summary.

Enables AI assistants to interact with Hadoop Hue for executing SQL queries using Hive, SparkSQL, or Impala and managing HDFS files. It supports directory browsing, file transfers, and exporting query results to CSV through the Model Context Protocol.

MCP server that provides AI assistants with tools to understand, validate, generate, and transform CERIF 1.6 research information, including entity browsing, XML validation, document building, and migration from CERIF 1.5.

Enables fetching any public web page through managed proxies, with optional JavaScript rendering, returning markdown, text, HTML, or structured JSON.

MCP server for EMQX that exposes 39 tools across 7 categories for managing clusters, clients, topics, messaging, access control, diagnostics, and data integration, with a web GUI and layered safety controls.

Enables unified querying of GA4, Cloudflare Web Analytics, Vercel Analytics, and Google Search Console through a single MCP interface, with cross-tracker comparisons and discrepancy reporting.

It gives Claude Code, Cursor, and Codex structured workflows and an MCP server for building data pipelines with dlt - covering REST API ingestion, transformations, data quality, and exploration.

Connects AI assistants to a self-hosted Your Spotify instance and Spotify's Web API for deep listening analytics and playback control. It enables users to query unlimited listening history, generate custom Wrapped summaries, and manage playlists through natural language.

Enables users to convert structured data into Token-Oriented Object Notation (TOON) to reduce LLM token usage and costs by up to 70%. It provides tools for encoding, decoding, and analyzing data formats like JSON, CSV, and XML to optimize prompt efficiency.

Minimalistic MCP server that lets AI assistants inspect, quality-check, and clean CSV datasets through tools, resources, and prompts, without needing local file access.

Enables validating CSV structure, checking simple schemas, converting between CSV and JSON, sampling rows, and finding duplicate keys on local files. All processing stays local, so no user data is ever uploaded.

Converts JSON data to TOON (Token-Oriented Object Notation) format and back, reducing token usage by 30-60% for more efficient LLM applications.

Enables AI assistants to interact seamlessly with IBM watsonx.data lakehouses using natural language for operations like querying, catalog browsing, engine management, and data ingestion.