"Network Requests" matching MCP servers:

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.

Provides powerful symbolic mathematics and statistical analysis capabilities, enabling users to solve equations, compute derivatives and integrals, perform statistical tests, and analyze data through natural language requests.

An MCP server that turns chart requests into permanent, hosted Image-Charts URLs, enabling agents to generate charts that render anywhere without client-side libraries.

MCP server for air-Q air quality sensors, enabling querying and configuring devices on the local network.

A fully offline MCP server for generating 15 types of SVG charts, including line, bar, pie, and more, without any network dependency.

Comprehensive Model Context Protocol server for advanced graph analysis using NetworkX, enabling graph creation, manipulation, and algorithm execution through natural language.

An MCP server that lets an AI assistant drive Epistemic Network Analysis models hosted on the webENA cloud service, enabling project listing, model running, and result visualization.

Enables creating themed SVG maps, GeoJSON data, projections, React components, and embeddable map builders through natural language requests.

Converts plain-English stock requests (e.g. "compare Apple and Microsoft YTD") into validated yfinance/matplotlib scripts, AST-checked and executed in a sandbox to produce chart PNGs. Exposes parsing, saving, execution, and history-listing tools to Claude Desktop and Claude Code through FastMCP, backed by local Ollama/DeepSeek-R1 or cloud Groq/GPT-4o models.

Provides local, deterministic Four Pillars (Bazi) chart calculation via MCP tools, with JSON and PNG outputs, no API key or network required.

Enables local offline machine learning workflows, including CSV profiling and cleaning, classifier and neural network training, hyperparameter optimization, evaluation metrics, confusion matrix plots, Streamlit dashboards, and HTML reports through MCP tools.

Renders PyTorch neural network architecture diagrams using VisualTorch, supporting multiple styles and customizable options.

Exposes Yandex DataLens operations as tools for Claude, enabling API health checks, raw requests, entry listing, and dashboard creation.

Enables natural-language stock analysis by converting prompts into validated ticker and timeframe requests, then calling MCP tools to fetch market data, compute returns and risk metrics, and render comparison charts. It supports multi-ticker comparison, normalized or cumulative returns, and optional clarification or critic review.