"A guide on how to edit videos" matching MCP servers:

Publishes a spending-summary tool alongside an interactive HTML view, letting people click a category in the answer to fetch the individual expense records behind that total. Keeps calculations and their supporting evidence together in a single inspectable flow.

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.

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.

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.

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

Renders OHLCV data as candlestick chart PNGs and returns inline images. It is a pure renderer with no network access, allowing models to view charts generated from provided data.

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.

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

An MCP server that lets Claude or ChatGPT read, create, and edit mind maps stored in a GitHub repository, with support for local and HTTP transport.

MCP server that enables natural-language-driven, headless HEC-RAS modeling, letting agents inspect, edit, run, compare, and plot 1D/2D hydraulic models without the GUI.

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.

Computes biorhythm values (physical, emotional, intellectual) and generates a PNG chart showing sine curves based on birth date and optional target date.

Enables Claude Code to create and edit presentation-grade visualizations such as cloud architecture diagrams, charts, KPI dashboards, and poster infographics, with auto-layout, themes, and export to SVG, PNG, JPG, and PDF.

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.

Data-visualization MCP server for ROOT Workspace. It enables creating and iterating on charts (Vega-Lite, Python plots) with a persistent kernel and vision loop.

Enables MCP clients to teach on a view-only local browser chalkboard by drawing equations, plots, molecules, diagrams, and science illustrations step by step, with snapshots and saved lessons.

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.

MCP server that automates the publishing and validation of Tableau workbooks and data sources on Tableau Server or Tableau Cloud, enabling an AI agent to discover, build, validate, and publish content without human intervention.

Enables annotating SVG paper figures in the browser by picking elements or drawing regions, and feeds those annotations back to Claude via MCP so it can edit the underlying SVG or generation scripts, with automatic page refresh on changes.

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.

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.

Enables Claude to build and manage Zuar Portal HTML blocks through the Portal REST API, including discovering datasources, previewing data, and performing CRUD operations on blocks.

A Model Context Protocol server that provides a dynamic map interface with tools to add raster/vector layers, control map view, and modify paint properties.

Turns JUnit XML test reports from CI into queryable QA metrics, letting AI assistants answer plain-English questions about failures, flaky tests, regressions pinned to a commit, root-cause clusters, and performance drift. Also generates ready-to-share QA health reports and self-contained HTML dashboards without logging into separate tools.

An unofficial MCP server for generating infographics and visuals from text using the Napkin AI API, enabling AI assistants to create professional diagrams, mindmaps, and charts.

Enables engineering leaders to query their AI assistant for quality KPIs — MTTR, MTTA, DORA metrics, defect escape rate and SLA breaches — computed from Jira bug, incident and deployment CSV exports already on hand. It delivers trends, period comparisons, hotspot findings and ready-to-share executive summaries and HTML dashboards without a BI stack or data warehouse.