"How to create charts from data" matching MCP servers:

An MCP server that generates and edits images using OpenAI's GPT Image model, allowing users to create images from text descriptions and edit existing images through natural language.

Enables AI agents to generate and render charts as PNG, SVG, or WebP images directly in chat interfaces. Supports Chart.js specifications and natural language descriptions for creating visualizations from data.

Enables LLMs to interact with Sisense data models and create charts programmatically via natural language, supporting tools for data sources, fields, and chart building.

A deployable remote MCP server that packages Microsoft's Flint chart compiler for chat hosts, enabling users to render, compile, and explore charts from natural language descriptions across Claude and Codex.

Enables AI assistants to perform Google searches with real, resolvable sources and transparent search terms.

Enables classification of GPU workloads as training, inference, or idle from telemetry data, with tools for one-shot classification, benchmarking, and audit log verification.

Enables agents to get fast, calibrated probabilistic answers from Jev (Typesafe AI) to yes/no, scale, or choice questions about provided material, without using a generative model.

MCP server that lets AI assistants like Claude or Cursor extract structured data (e.g., invoices) from images and PDFs via Vision API, with tools for analysis, questions, and preset management.

An MCP server that enables Claude Code to generate images and short videos using Google's Gemini API (Imagen 4 for stills, Veo 3.1 for video). It integrates directly into development workflows, allowing AI assistants to create visual assets and reference them in code without context switching.

An MCP server that gives text-only models image generation and understanding capabilities, with tools like generate_image, describe_image, and set_api_key. It enables any MCP client to create and analyze images using a single API key.

An MCP server that protects AI agents and RAG systems from adversarial poisoning attacks like prompt injection, steganography, and data anomalies through multi-layer sanitization, entropy-based detection, and consensus verification.

Superhuman data-driven science. Allows agents to upload any tabular dataset, specify a target column, and get validated predictive patterns (with p-values, effect sizes, and context from literature) that surface feature interactions and subgroup effects you'd otherwise miss. Many discoveries already made, free for open data!

Multi-model fusion analysis tool that sends queries to multiple AI models in parallel and returns structured analysis from a judge model.

Generate AI UGC videos for your app from any MCP client: create an AI influencer, make her photo, turn it into a talking 5 or 10 second video, add your app screen recording and a hook line, download the file. Needs a ClipUGC API key.

Enables MCP clients to rent GPUs from ParalonCloud using natural language, including browsing available GPUs, starting and stopping rentals, and retrieving connection URLs.

Enables using DeepSeek models as a small, cheap supervised worker from any MCP-compatible client, providing fast flash and deep reasoning tools for bounded tasks.

Provides access to EraseText's AI text removal for images, enabling erasing text from photos and retrieving account details.

Enables users to select the optimal LLM for their specific task by aggregating benchmark data and user intent, returning a ranked shortlist with plain-English rationale.

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

Enables coding or reasoning agents to request structured judgments from TypeSafe's Jev model at decision points, including choices, scores, claim verification, and code reviews, with probabilities and confidence returned as data.