Enables auditing chart color palettes for colorblindness accessibility by simulating CVD and measuring contrast. Provides tools to audit palettes and design tokens mid-conversation.
Enables generation of 25+ types of charts and data visualizations using AntV, including bar charts, line charts, maps, mind maps, and specialized diagrams like fishbone and sankey charts. Supports both statistical charts and geographic visualizations for comprehensive data analysis and presentation.
Enables AI assistants to convert CSS-animated SVGs into videos (MP4/WebM/MKV/MOV) and animated images (aPNG/GIF) with frame-accurate rendering and transparency support.
Gives LLM agents spatial awareness inside Autodesk Maya, enabling scene inspection and measurement, aesthetic and engineering audits, camera planning, checkpoint/rollback safety, asset import, and viewport or render previews. Agents can also execute arbitrary Python in Maya to build and verify 3D scenes against engineering rules instead of writing blind.
Use Codex as a subagent inside Claude Code: async dispatch into a long-lived codex app-server, results pushed back into the Claude session automatically, steer/interrupt mid-task, and Codex's built-in code review.
Text-to-speech MCP server that enables AI assistants to read text aloud on the user's computer using Windows SAPI, with no API key or cloud service required.
Enables AI agents to subscribe to a deterministic daily ethics feed drawn from a four-module curriculum and retrieve read-only tools, resources, and reflection prompts covering principles, case studies, and codes of conduct.
Enables extraction of numeric data points from visual bar, line, and scatter charts by calibrating coordinates and recovering structured tabular datasets from document payloads. Returns validated matrices and audit telemetry to MCP-compliant clients such as Claude Desktop and Cursor.
Enables AI agents and MCP-compliant clients to extract visual data from charts and graphs into structured markdown tables using deterministic, zero-dependency Python.
Turns a table your coding agent already has β a CSV, dataframe or semantic-model query result β into chart code that fits it first try, by profiling the data locally, ranking roughly 90 chart types by what it can honestly show, and returning verified D3, Plotly or matplotlib source you own and can run anywhere. It can also find the natural views inside a Microsoft Fabric semantic model and say which chart suits each.