"A method for finding people on LinkedIn and organizing them in a CSV" matching MCP servers:

An HTTP MCP server for exploring CSV files that masks PII before any code execution, enabling safe data analysis with local or cloud LLMs.

Enables LLMs to interact with Kibana through composable toolboxes for building dashboards, managing data, and handling alerts, among other operations.

A local, fully vectorized computational engine for stock market analysis that enables AI to perform factor calculation, strategy backtesting, IC analysis, and GPU-based multi-dimensional visualization using local DuckDB data.

A safety-gated Python MCP server for Metabase that lets MCP clients and agents inspect Metabase, run governed queries, and create or maintain dashboards, cards/questions, collections, snippets, permissions, and other Metabase assets through the Metabase REST API.

Provides aerodynamic analysis tools through MCP, enabling geometry generation, meshing, CFD solving, and visualization for 2D airfoils.

A clean-room SHARP-on-MCP compliant FHIR R4 MCP server that enables AI agents to interact with any FHIR R4 endpoint using SHARP context headers, without server-side OAuth. It provides clinical tools, lab results, imaging, and interactive MCP-UI dashboards.

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 your own ChatGPT to build and manage a collaborative work board—dashboards, categories, datasets, monitors, and policies—through typed WebMCP tools, with human approval, audit trails, and guardrails enforced on every action.

MCP server for Microsoft Power BI and Fabric, enabling Claude to discover workspaces, semantic models, generate and execute DAX queries, and retrieve report metadata via device-code authentication.

Enables AI assistants to drive a live TradingView chart in Chrome, turning natural-language trading ideas into compiled Pine Script indicators, adding them to charts, and reading back results. It also supports chart navigation, indicator management, screenshots, and strategy tester backtest results.

MCP server providing data analytics tools for AI agents to load, clean, analyze data, generate charts and dashboards.

Enables AI assistants to control a logged-in TradingView chart in Chrome, including writing and compiling Pine Script indicators, adding them to charts, changing symbols and timeframes, capturing screenshots, and retrieving strategy tester results.

Options analytics an agent can drive: import a permitted chain, solve implied volatility, build a sixteen-Greek ladder, map dealer gamma walls, rank twenty-three structures, run a GARCH-t simulation with a convergence gate, and backtest with modelled premiums. Twelve stdio tools; every output is a schema-validated JSON file with source, assumptions and a degraded flag. Research only, no orders.

Embeds live, interactive Tableau dashboards in ChatGPT, pushing mark selections to the model so questions about specific data points are answered with live queried numbers.

Renders word-sized charts — sparklines, bars, bullets and 100+ more types — to self-contained SVG with generated alt text, small enough to sit inline in a model's reply or a table cell. Three tools: find the right chart for a question, get its wiring, and render it.

Calculates Taiyi Shenshu (太乙神數) based on a given datetime and returns structured text and a board PNG image.

Zero-dependency charts for Python and AI agents. SVG and PNG, 15 chart types, a one-line MCP server, and a Claude skill.

A demo MCP server that returns an interactive dashboard (bar chart with dataset selector) as an MCP App via the official MCP Apps extension (SEP-1865).

MCP server that lets agents create, display, and export rich UI widgets (cards, dashboards, charts, forms) inline in conversations, with interactive iframe support in MCP Apps hosts and PNG image fallback for other clients.

A Model Context Protocol server that gives an LLM a structured visual layer to describe what exists and get back a clean, precise SVG diagram instead of ASCII art.