"How to look up websites using a search engine without APIs" 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 discovery of Fabric/Power BI workspaces and semantic models, execution of DAX queries, and generation of executive HTML reports with visualizations.

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 an agent to turn a paper's draft into editable Figma frames for publication-ready figures, checking every number against the paper and verifying the exported PDF.

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.

Connects Google Search Console to AI assistants, enabling SEO data analysis through natural language conversations.

Enables searching Google Scholar papers with year filtering, deduplication, and proxy support. Also provides paper detail retrieval, TF-IDF relevance analysis, and chart generation via MCP tools.

Google Search Console for Claude and other MCP clients. Sign in with Google in one click, then ask about clicks, queries, rankings and indexing, export every GSC report as CSV (beyond the 1,000-row UI limit), inspect URLs, submit sitemaps and verify sites. Includes a local SEO dashboard. Free, open source, runs on your computer.

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.

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

Lets agents and chat clients load battery cells, collect them into frames, render figures, export data, look up cellpy API calls, and set up batch projects without writing Python.

Integrates with Google Search Console to enable querying search analytics, comparing performance periods, generating visual reports, and identifying SEO optimization opportunities through natural language.

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 read-only analysis of FST waveform files, including signal value queries, timing measurements, event search, latency analysis, and PNG plot generation using human-readable time addressing.

Enables rendering, validation, formatting, and inspection of D2 diagrams via the Model Context Protocol, using WASM for rendering without requiring the d2 binary.