"A server for searching the web" matching MCP servers:

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.

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.

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.

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

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.

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).

Enables MCP-capable LLMs to generate and render ephemeral Prefab UI interfaces at runtime, with tools for generating UI code and searching Prefab components.

Turn any CSV into a stunning interactive dashboard with one command — works as CLI, MCP server, or AI skill

Enables searching Google Scholar papers with year filtering, deduplication, and proxy support. Also provides paper detail retrieval, TF-IDF relevance analysis, and chart generation via MCP tools.

MCP server for AI Diagram Maker — generate software engineering diagrams from natural language, code, ASCII diagram, images, or Mermaid. Inline diagram rendering using MCP apps UI and diagram URL in responses. Works with Cursor, Claude Desktop, Claude Code, and any MCP-compatible AI.

MCP server for managing a task graph with live visualization. Enables Claude Code to create, update, and link nodes (tasks, subtasks, ideas, etc.) while a browser viewer displays the graph in real time.

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.

Wraps a climate data-processing pipeline so agents can inspect the config schema and available sample CSVs, validate an inline JSON config, and run the pipeline to return a monthly summary table plus a rendered plot, with file access sandboxed to the data and outputs directories.

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.

Turns a CSV — or a public dataset named in plain English — into an animated chart video. Every numeric claim in the generated narration is recomputed from the source rows before export, and a claim that does not reconcile blocks the export rather than shipping.

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.

Enables AI agents to build freeform dashboards for e-ink panels by listing widgets and devices, laying out a canvas, rendering a preview, and pushing to the panel.

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.

Enables AI hosts to generate editable online diagrams (flowcharts, mindmaps, UML, and more) from natural language or Mermaid, using a ProcessOn account via the Model Context Protocol.

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

Open-source dotted maps and animated 3D globes. Pick one of 21 looks for a country or the world, build share links, open and edit a pasted link, and get embed code. Hosted at https://globestudio.app/mcp or run with npx @globestudio/mcp. Free, MIT, no API key.

diagramzu-mcp lets an AI client (Claude, Cursor, ChatGPT, etc.) author Mermaid diagrams in a shared workspace that a team can read, comment on, embed, and present at a clean URL — rather than just generating a one-off diagram. Hosted endpoint https://mcp.diagramzu.ai/mcp or local npx -y @diagramzu/mcp. Also listed in the official MCP registry as ai.diagramzu/mcp.

Enables assistants to read and analyze construction schedules like a scheduler: CPM, float, critical paths, DCMA 14-point, logic checks, update-to-update comparison, baseline variance, and S-curves from P6 XER and MS Project files.

Gives terminal coding agents a local browser canvas for rendering charts, tables, metrics, markdown, live widgets and sandboxed MCP App UIs that would otherwise reach them as raw JSON. Lets agents pose blocking visual multiple-choice questions and read back the user's clicks, selections and an animated architecture map of file activity.

An MCP server for the Xe Currency Data API that brings live FX rates, historical analysis, and quant-flavored tools into AI tools, working out of the box with zero credentials via Frankfurter/ECB data.

Visualizes conversations as structured hierarchical mind maps by injecting a prompt that instructs the LLM to generate a JSON mind map — no external APIs required.

Lets AI agents turn an existing STL, a sketch, or a plain-language description of a part with its mounts and loads into a topology-optimized, printable STL. Includes problem checks with time estimates, a live dashboard of the run, fine passes, and print/stress verification that returns the final mesh and mass, stiffness and run-time metrics.