"LinkedIn company profile pages" matching MCP servers:

A unified Model Context Protocol server for data engineering and analytics that lets LLM clients query, profile, transform, and visualize data using DuckDB and PySpark, while keeping raw data out of the model's context through read-only, bounded, and audited operations.

Turns plain text or Markdown, web pages, and local PDFs into editable Markdown outlines that can be rendered as standalone, offline-capable interactive HTML mind maps. Works with any MCP client with no API key required, since the connected model writes the final outline from a draft and cleaned source text.

Generates pure CSS and SVG charts for web pages and markdown docs, eliminating the need for chart libraries, and provides injection guidance.

Enables natural language management of Zuar Portal instances, including authoring HTML blocks, building pages, managing data sources, queries, themes, and users, with versioned and revertible changes.

Enables MCP-capable agents to profile datasets and render CSV/TSV/TXT/JSON data into standalone interactive ECharts HTML charts fully offline, returning the HTML artifact plus rendering statistics in the same response.

Enables AI assistants to query Oracle AI Database read-only, profile tables, and render result sets as PNG charts.

Enables MCP-compatible clients to query advertising data, analyze spend, conversions, and audiences, compare campaigns, generate dashboards, and manage campaigns with optional read-only safeguards.

Build Power BI reports with natural language through an MCP server that connects AI assistants to Power BI's PBIR file format for creating pages, visuals, themes, and filters locally.

Enables coding agents to turn finished sessions into Strava-style achievement cards with distance, elevation, effort, badges, and personal records, plus profile, activity feed, season recap, and leaderboard tools—all measured from session transcripts rather than self-reported.

Enables AI assistants to create Evidence reports and dashboards by providing tools to query database schema, read documentation, edit pages, and debug code.

Enables AI assistants and editors to profile, clean, and analyze messy CSV/Excel files directly through MCP tools such as profile_dataset, clean_dataset, and list_sheets.

Enables an AI assistant to build Power BI projects end to end: data modeling (tables, DAX measures, relationships, calculation groups, roles, field parameters) against an open Power BI Desktop via TOM/XMLA, and reports (pages, visuals, formatting, bookmarks, themes) as PBIR files in a .pbip project. Every visual is cross-checked against the real model before being written, so generated dashboards don't break on open.

MCP server that turns coding agents into data analysts: attach CSV/Parquet/JSON/XLSX or databases, profile data, run read-only SQL, and render Vega-Lite charts as SVG files.

Enables AI agents to generate safe, runnable HTML chart pages from structured JSON data using Apache ECharts. It provides tools for chart type recommendation, page generation, validation, and patching with controlled, deterministic output.

Enables AI agents to synthesize heterogeneous search results into interactive comparison layouts (Genspark Sparkpages) through the Model Context Protocol, with deterministic, zero-dependency execution.

Enables deterministic multi-timeframe XAUUSD market analysis and TradingView chart automation through MCP, allowing AI assistants to read charts, manage Pine Scripts, and execute analysis workflows with profile-gated access.

An MCP server for generating offline charts using Apache ECharts SVG SSR and rsvg-convert, optimized for Kylin ARM64 with 64KB page size.

Provides tools to inspect dataset schema, profile, preview, and execute read-only SQL queries on uploaded CSV/Excel files.

A production-grade MCP server that provides tools to fetch historical stock data, company information, and technical indicators (SMA, EMA, RSI, MACD, Bollinger Bands) via yfinance, and includes a Flask web dashboard for visual analysis.

Enables AI agents to perform deterministic search synthesis and compile Sparkpage layouts into comparison matrices and ready-to-render HTML interfaces through a zero-dependency Python MCP server. Plugs into Claude Desktop, Cursor, and other MCP clients for structured, low-latency output with execution telemetry.