"The dio-odi-mcp server" matching MCP servers:

Enables dynamic creation and execution of custom tools/functions in multiple programming languages at runtime, exposing them to MCP clients like Claude.

Enables executing Python scripts, rendering scenes, and controlling 3D objects in Blender via Claude, with support for Cycles and EEVEE engines.

Enables AI agents to directly manipulate Jupyter Notebook cells and structure through a secure, well-defined API, overcoming Cursor's AI agent mode limitations.

A framework that provides search, fetch, and do primitives to enable AI agents to orchestrate complex tasks via sandboxed TypeScript code execution. It optimizes performance and security by reducing multiple sequential tool calls into a single, efficient inference round-trip.

Give your AI app any GitHub link and the job you want done. UGT gets the project running on your computer and hands you the result.

An MCP server that wraps the Riza Code Interpreter API and presents endpoints as individual tools.

Integrates ElevenLabs Text-to-Speech capabilities with Cursor through the Model Context Protocol, allowing users to convert text to speech with selectable voices within the Cursor editor.

Hardened MCP bridge to the full Ollama ecosystem — local and cloud models. 6 tools covering health, model management, chat, and generation with SSRF mitigation, singleton HTTP client, and structured error handling. Two deps, one file.

A persistent Python REPL MCP server for AI agents with stateful execution, real timeouts, crash isolation, and an MCP bridge to call other tools in the project.

Enables AI assistants to run code in 24 programming languages on HackerEarth's sandboxed servers and read the printed output or errors, without installing local toolchains. It also lists supported languages with exact identifiers for use when submitting code.

Enables MCP-compatible AI assistants to run Python code on Google Colab GPU/TPU runtimes, supporting accelerators like T4, A100, H100, with background execution and Google Drive integration.

Enables a model to write short Python programs that call a wrapped MCP server's tools as async functions, rather than invoking them one at a time. The program runs in a locked-down Docker sandbox, and every tool call passes through a host-side bridge that enforces an allowlist, schema-validated arguments, call budgets, timeouts and output caps.

MCP server for Archicad automation, enabling AI assistants to run Python scripts against running Archicad instances via the Tapir JSON API for complex workflows.

Enables agents to interact with the Logflare API by writing JavaScript functions that run in a Vercel Sandbox, using three tools: search, execute_read, and execute_write.

An MCP server that keeps raw tool output out of the context window by sandboxing and compressing results (about 98% reduction), persisting file edits, git operations, tasks and decisions in SQLite with BM25 retrieval so the agent resumes after compaction, and enforcing routing across 17 platforms via MCP and hooks. It also encourages the model to write scripts that compute answers and log only results instead of reading large data into context.

An MCP server for running Claude Code and the Codex CLI as a pair: Claude drives, and hands self-contained tasks to Codex as background jobs.

Exposes the Grok Build CLI as MCP tools for Claude Code and other clients, enabling headless AI agents to perform tasks like code reviews, web research, and file operations with configurable permission levels.

Delegates coding tasks to the locally installed Kimi Code CLI, reusing its auth, models, and configuration while adding tools for sessions, replies, model discovery, live turn steering, and history access.

Enables chat assistants to insert graded coding exercises (Python, JavaScript, TypeScript, SQL) with hidden tests, and returns failed attempts to the conversation so the tutor can explain the learner's specific mistake.

Enables coding agents to gate code modifications behind an evidence check, preventing unnecessary edits by requiring reproduction of the problem before allowing changes.

A Model Context Protocol (MCP) server that wraps the dbt CLI tool, enabling AI coding agents to interact with dbt projects through standardized MCP tools. Developed by Mammoth Growth.

Enables coding agents to compile supplied files, run optional focused tests, and get static findings across ten programming languages, providing a deterministic feedback loop on the code they are about to return.

Lets your AI find the open-source tool it needs on GitHub and install it as an MCP tool, in a sandbox, after you approve. Reads only the folders you grant; no network unless allowed.

Enables AI coding agents to safely run commands, install dependencies, test, and modify code inside disposable, policy-enforced Docker sandboxes isolated from the host, returning results and diffs.

Enables AI agents to drive a running Roblox Studio in real time over a single WebSocket, executing whole Luau programs, installing in-engine controllers, hot-patching scripts during live playtests, and receiving errors, assertions and other events as pushes. It also reaches Roblox Open Cloud for the open place and answers visual questions via a vision sidecar, exposing all of this through nine MCP tools.

Code-mode MCP server (docs_search + execute_code two-tool surface) backed by a unified capability manifest across three sandbox kernels (in-process node:vm, WASM via QuickJS / Pyodide / Wasmtime, and remote microVM via E2B / Cloudflare Sandbox). At N=30 tools the bootstrap-context cost drops to 13.6% of direct tool-use. Apache-2.0.

Lets Claude write and run short JavaScript programs against the YouTube Data, Analytics, and Reporting APIs, plus transcripts and search autocomplete, so multi-step channel tasks like joining upload stats with analytics finish in a single round trip. Code executes in a sandboxed isolate with quota tracking and confirmation gates for destructive or public actions.

Finds slow pandas code (iterrows loops, row-wise apply, concat in a loop), runs a rewrite on the same data to check the result is identical, and measures the speedup. Tools: check_code, verify_rewrite, list_rules.

Local CI/CD engine for AI agents. ActionD listens to LGH git events and runs plugin jobs (Go/Python/Java/web toolchains) in isolated checkouts, with structured results, root-cause failure diagnosis, approval gates, and auto-rollback. The MCP server exposes 23 tools, including dev_cycle_run (commit → CI → structured verdict in one call) and actiond_diagnose for failed-job analysis.

Enables AI coding agents to safely execute commands, run tests, and modify project files inside disposable, policy-enforced Docker sandboxes that are isolated from the host machine and its credentials.

Enables AI agents to drive the Opiumware Roblox executor on macOS by running Luau scripts and retrieving return values, prints, and errors. It also exposes tools for reading console logs, searching decompiled client scripts, capturing remote network traffic, and taking screenshots so agents can verify and iterate autonomously.

An MCP server that bridges any MCP-capable AI host to Roblox Studio. The AI executes Lua live in Studio, reads output/errors, and iterates.

Enables users to create, inspect, analyze, format, and chart Excel workbooks by having the client LLM write Python that runs in a fresh, network-restricted Docker container. Exposes a single execute_python tool with access to openpyxl, pandas, numpy, and matplotlib, persisting files between calls.

An MCP server that exposes the Prova reasoning verifier, enabling AI agents to verify their own reasoning and kernel-check Lean 4 proofs before outputting answers.

Enables serving a directory of Agent Skills to MCP clients, listing available skills, delivering their instructions and bundled files, and running only the scripts each skill explicitly declares under strict execution limits.

Enables AI-powered instant code merging at 10,000+ tokens/sec and agentic codebase search with natural language queries through the Relace API.

A secure, containerized workspace for LLMs to autonomously code, test, and debug projects without risking the host machine.

Enables running Python and commands inside isolated, persistent Wasmer sandboxes on the host, while also exposing an Edge-deployed Postgres-backed service for storing notes and collecting feedback.

Enables AI coding agents to safely execute code, run tests, and build projects inside disposable Docker sandboxes, protecting the host machine through enforced isolation, filesystem snapshots, and network controls.

MCP server for grounding verification (exact quotes, citations, code output, repo patterns, arithmetic) using no LLM in the verification path.