"How to create a sandbox environment" matching MCP servers:

Provides a secure, containerized Python sandbox for executing LLM-generated code with multi-layer isolation, along with JSON/CSV validation and workspace state snapshots.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

A runtime-free MCP server that converts source code into AST🌲, regardless of language.

Turns GitHub repository history into a cited maintainer skill for coding agents, providing tools to collect evidence, query the knowledge graph, and inspect bundles.

Enables Claude to use Google Gemini as a secondary AI through MCP for large-scale codebase analysis and complex reasoning tasks. Supports both Gemini Flash and Pro models with specialized functions for general queries and comprehensive code analysis.

Enables automated migration of Vue projects to React with intelligent analysis, task management, and learning from user modifications.

Enables AI assistants to analyze project folders for dependencies and frameworks and fetch current documentation or datasheets from the web.

Converts vector PDF circuit schematics into evidence-preserving SchematicIR JSON with tools to inspect, convert, and validate schematic PDFs.

Enables migration of test automation projects from WebDriverIO to Playwright using AST-based transformations. Provides tools for analyzing tests, converting syntax, refactoring to Page Object Model, and generating migration reports.

A lean, local knowledge graph that joins a repo's code to its aSPARK delivery artifacts, enabling agents to trace user stories to code and assess impact of changes, served over MCP.

Enables AI coding agents to extract Figma design data and convert it into Flutter widgets and screens. Supports theme setup, component analysis, asset exports, and provides Flutter implementation guidance from Figma designs.

Zero-dependency MCP server inspector for protocol conformance, tool annotations, and capability checks.

coldstart gives coding agents durable memory of a specific codebase, with no embeddings, no vector store, and no API key. Agents write notes after real tasks — what a file is for, how a flow spans files, which invariants hold — and those notes are surfaced automatically to a later session whose task matches, so nothing gets rediscovered from scratch. Every note is anchored to real files by content

MCP server for Speakeasy — Windows API emulation for binary analysis. It runs a .exe in a Wine-like sandbox and returns a structured API trace.

Call graph MCP server for Rust, Python, TypeScript, TSX, and JavaScript. Every caller and callee carries a resolution field showing how the reference was matched to a definition, plus multi-hop impact analysis for refactoring. Local-only, single binary, no network egress. Benchmarked against grep, ripgrep, and ctags on a 120-item hand-verified golden set, including the cases where it loses.

On-demand micro-mutation sandbox for AI test verification that maps weaknesses in unit tests by running isolated mutation testing via the Model Context Protocol.

A local-first MCP preflight system to help AI inspect Python repositories before generating code, enabling reuse, extend, or create decisions. Currently in early development with only foundational modules implemented.

An MCP server that provides a persistent sandbox for AI coding agents to explore codebases server-side, returning only compact summaries to reduce context consumption.

locidx

A codebase ontology and a cited wiki, both kept as Markdown in one folder on your disk. Typed graph answers — what a change affects, how two concepts connect, which wiki lines lack a citation — and every write lands as a Git diff a person reviews.