"SearXNG, a privacy-respecting metasearch engine" matching MCP servers:

A graph database MCP server that lets AI assistants build, analyze, and visualize relationship graphs with algorithms like PageRank and cycle detection.

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.

Enables natural-language-driven analytics over CSV/Parquet or built-in data by decomposing questions into analytical tasks, running them via deterministic DuckDB and SciPy tools, and publishing only claims verified against the underlying rows.

A statistical analysis MCP server offering 30 tools for descriptive statistics, hypothesis tests, regression, and time series, all returning Markdown reports with automatic interpretations to enable AI agents to perform comprehensive data analysis.

Enables AI agents to perform deterministic search synthesis and compile Sparkpage layouts into comparison matrices and ready-to-render HTML interfaces through a zero-dependency Python MCP server. Plugs into Claude Desktop, Cursor, and other MCP clients for structured, low-latency output with execution telemetry.

Enables AI agents to perform dynamic search synthesis and compile Sparkpage layouts into comparison matrices and HTML interfaces, running deterministically with zero external dependencies over the Model Context Protocol.

Enables AI assistants to authenticate against a cloud geospatial analysis platform, search and inspect its datasets, render them as maps, and compute statistics over specified regions. It exposes these capabilities as MCP tools that can be dropped into any Next.js project via a route-based adapter.

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.

An HTTP MCP server for exploring CSV files that masks PII before any code execution, enabling safe data analysis with local or cloud LLMs.

Enables conversational spatial SQL queries in plain English, turning natural language questions into validated PostGIS operations and rendering results as GeoJSON on an interactive map.

Enables AI assistants to read and modify Power BI Desktop files programmatically, including querying models, creating measures, and managing relationships through natural language.

Geneva MCP brings production-grade forecasting directly into AI assistants and coding agents. Connect any MCP-compatible client to the Geneva Forecasting Engine and run rigorous time series forecasts through natural conversation.

Enables any MCP client to evaluate Wolfram Language code in a persistent local Mathematica or Wolfram Engine kernel, export results to common file formats, and inspect or manage kernel status without cloud APIs, API keys, or runtime dependencies.

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.

An MCP server that connects AI assistants to a local Wolfram Engine, enabling symbolic math, numerical analysis, and data visualization through Wolfram Language. It provides secure expression filtering, client authentication, and supports both local stdio and HTTP transports.

Exposes NeuroGraph's scientific API to AI models as MCP tools, enabling them to query, analyze, and visualize brain networks — regions, tracts, connectivity, literature, and cross-species homologies — drawn from a portable SSD-based neuroimaging data library. Lets models read real atlas data, load datasets, compute graph metrics, and produce visualization scene descriptors without acting as the scientific engine itself.

Enables AI agents to query a privacy-focused analytics service for aggregate stats, breakdowns, realtime visitors, and time-series trends across cloud or self-hosted deployments. It also provides connectivity checks and guided analytics playbooks.

Viznoir empowers AI agents to directly analyze and visualize complex 3D scientific and engineering data in cinematic resolution using a fully headless VTK engine. It enables LLMs to seamlessly execute advanced physical analysis, 3D rendering, and generate publication-ready animations purely through natural language commands.

Enables Claude Code and other processes to control the lys interactive multi-dimensional data analysis and visualization platform by executing Python commands in it and viewing the resulting lys and matplotlib graphs. It connects over a local socket or TCP to a lys instance running in remote mode.

Enables MCP-compatible agents to render privacy-first, deterministic Chart.js charts locally as PNG, SVG, PDF, or JSON, with configurable Sotheby's-inspired editorial themes per session.