"Unity game development engine and related resources" matching MCP servers:

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.

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

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.

Enables plain-English analysis of game telemetry through tools for schema inspection, SQL queries, metric/KPI retrieval, anomaly sweeps, and synthetic data loading.

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.

Options analytics an agent can drive: import a permitted chain, solve implied volatility, build a sixteen-Greek ladder, map dealer gamma walls, rank twenty-three structures, run a GARCH-t simulation with a convergence gate, and backtest with modelled premiums. Twelve stdio tools; every output is a schema-validated JSON file with source, assumptions and a degraded flag. Research only, no orders.

Enables LLMs to generate visual summary reports from CSV datasets using MCP Resources, Tools, and Prompts, without exposing raw data to the model.

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.

MCP server that provides a live, terminal-native layered dependency map for AI coding assistants, with tools to track and visualize development progress in real time.

A production-grade MCP server for enterprise sales analytics, enabling LLM clients to query, analyze, and visualize sales data from a SQLite database through structured tools, resources, and prompts.

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.

An MCP server that enables AI assistants to query databases, execute SQL, and manage Metabase resources like dashboards, cards, and collections through natural language.

An MCP server for rendering common ECharts visualizations from structured tool arguments, exposing per-chart opener tools that map to independent HTML app resources.

A local Python MCP server for AI-assisted development of Yandex DataLens dashboards, enabling inspection, planning, validation, and guarded application of changes.