"Understanding the Concept of LLM Context" matching MCP servers:

Enables LLM clients to load, clean, visualize, and analyze tabular datasets, and to generate multi-format analytic reports through natural language instructions.

Provides AI agents with read-only access to live chart context, candles, drawings, indicators, and provider status from the MyLinedChart desktop app, all running locally.

Exposes semiconductor wafer analysis tools including wafer maps, P-charts, and statistical plots, enabling AI assistants to visualize and analyze wafer test data.

A Model Context Protocol server for managing and analyzing investment portfolios. It enables users to create and update portfolios, fetch real-time stock data and news, generate performance reports, and receive investment recommendations through natural language.

Enables turning data into map images of countries, states, provinces, counties, continents, and the world, including choropleths, category maps, and pinned locations.

Enables rendering, validation, formatting, and inspection of D2 diagrams via the Model Context Protocol, using WASM for rendering without requiring the d2 binary.

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.

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.

Enables converting ASCII and Unicode text diagrams into crisp, animated SVG with 1:1 verification, including repair of misaligned diagrams.

MCP server that lets agents create and update persistent concept maps via the NodeLand API, returning shareable URLs for humans to view, embed, and keep editing.

A Model Context Protocol server that renders Apache ECharts options to SVG, enabling AI agents to generate deterministic charts server-side without a browser.

Enables users to create, inspect, analyze, format, and chart Excel workbooks by having the client LLM write Python that runs in a fresh, network-restricted Docker container. Exposes a single execute_python tool with access to openpyxl, pandas, numpy, and matplotlib, persisting files between calls.

MCP server for generating diagrams, charts, and visualizations using Gemini image generation on Vertex AI. Supports auto-detection of diagram types, multiple style modes, and iterative refinement.

Enables LLMs to safely execute pandas code for data analysis and generate interactive visualizations through the Model Context Protocol.

A Model Context Protocol server for interacting with Lightdash, enabling AI assistants to discover data, create charts, and manage dashboards programmatically.

A FastMCP server that turns JSON data into Plotly figures, deployable as an isolated service on Modal. Data goes in as JSON; a validated Plotly figure (JSON) or a rendered PNG comes back over the Model Context Protocol.

Provides live weather radar and text-based radar maps for any location via the get_weather tool, returning structured data and character grid visualizations readable by both humans and LLM agents.

Render hierarchical system diagrams as PNGs from YAML definitions using a four-level ontology of networks, factories, machines, and nodes.

Generates beautiful Excalidraw diagrams from natural language descriptions using a local llama.cpp LLM, entirely offline.

Validates and renders declarative JSON diagram specs to themeable SVG. The format has no coordinates, so a model writes structure and the renderer does the layout; tools: validate, render, kinds.