"A sandbox environment to run Python code and install dependencies" matching MCP servers:

A Python MCP server for Metabase that enables AI assistants to query databases, manage dashboards, and interact with your Metabase instance.

Enables discovery of Fabric/Power BI workspaces and semantic models, execution of DAX queries, and generation of executive HTML reports with visualizations.

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 LLM clients to run Python code in a persistent, user-selected interpreter via MCP, with tools for data loading, summaries, regressions, diagnostics, and plotting, while keeping sessions alive between calls and isolating crashes.

An oil & gas data-analytics MCP server that enables guarded SQL queries, well valuation, and map rendering within chat apps.

An MCP server that generates Excalidraw diagrams to visualize algorithm execution step-by-step, supporting multiple data structures and integration with AI assistants.

Enables secure execution of statistical Python code in isolated micro-VM sandboxes, generating charts and analytical summaries.

Enables users to create, validate, view, and export system stock-and-flow maps from a versioned JSON model through MCP-capable hosts over local stdio or hosted Streamable HTTP. Supports causal-loop and nested-level mapping while keeping submitted model content with the caller and requiring no LLM provider key.

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 Codex to control a Gephi 3D network view through natural language, including network generation, 3D layout, node focus and neighbor highlighting, layered views, and PNG/PDF export.

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 time series analysis following Box-Jenkins-Treadway methodology, supporting guided or autonomous modes for model identification, estimation, and diagnosis via an LLM.

Turns relational schemas into editable entity-relationship diagrams in draw.io, supporting live databases, SQL DDL, YAML/JSON specs, and existing .drawio files, with tools for managing tables, columns, and relations.

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.

Renders word-sized charts — sparklines, bars, bullets and 100+ more types — to self-contained SVG with generated alt text, small enough to sit inline in a model's reply or a table cell. Three tools: find the right chart for a question, get its wiring, and render it.

A Model Context Protocol server that gives an LLM a structured visual layer to describe what exists and get back a clean, precise SVG diagram instead of ASCII art.

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

Enables natural language querying of brain volumes (NIfTI) with a fixed set of tools, returning visualizations and reproducible nilearn code.

Enables MCP-capable LLMs to generate and render ephemeral Prefab UI interfaces at runtime, with tools for generating UI code and searching Prefab components.

A read-only, cross-platform visualization layer for Cortex that renders memory, sessions, and code into six live reading angles (graph galaxy, 3D anatomical brain, execution trace, consolidation board, knowledge browser, and wiki) without ever writing a memory.