"Testing Local AI Agents in LMStudio for Computer Use and Code Execution Tasks" matching MCP servers:

Gemini-powered MCP server for automated code review, analysis, and documentation.

Enables exploring Microsoft Fabric / Power BI workspaces and semantic models, and executing ad-hoc DAX queries.

A calculator-focused MCP server template with tools, resources, and prompts built on NitroStack.

Enables AI-driven financial analysis including portfolio analysis, risk assessment, investment simulation, market intelligence, and report generation via NitroStudio.

Enables detection of AI-generated content from ChatGPT, GPT-4, and Gemini models through the BACH AI Content Detector API.

An MCP-powered AI agent that audits your Gmail for recurring subscriptions, detects silent price increases, and flags unused services.

A local MCP server that lets any LLM agent manage Pine AI tasks — negotiate bills, cancel subscriptions, resolve disputes, and make phone calls on your behalf.

Enables AI agents to control NetEase Cloud Music playback, login, search, and get personalized recommendations through official APIs.

A minimal MCP server template that offers calculator tools, resources, and prompts for learning NitroStack fundamentals, built with TypeScript and Zod.

An MCP server that transforms standard LLMs into autonomous investment bankers, enabling live market data retrieval, fundamental ratio calculations, DCF valuations, portfolio diversification assessment, and automated emailing of executive reports.

Enables code execution in 70+ programming languages and databases through the Onecompiler API, allowing users to run and test code directly from their MCP-enabled applications.

MCP server that parses web pages into clean markdown via Crawl4AI's HTTP API, exposing parse_page and health-check tools with configurable proxy and timeout.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Sandboxed Python execution with automatic dependency management. Executes Python scripts in isolated environments (bubblewrap or Docker) with PEP 723 inline dependencies, preventing host pollution.

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.

MCP server that enables cloud models (like Gemini/Claude) to delegate coding tasks to a local llama.cpp server, preserving cloud usage limits through an AI-powered code review loop.

Enables local forensic analysis of files by orchestrating system binaries (file, exiftool, strings, Volatility) via safe subprocess execution.

Enables AI assistants to query KumoRFM for predictive analytics on relational data, including graph management, natural language to PQL conversion, and training-free predictions.

Provides CodeQL language intelligence to AI agents via MCP, enabling completions, hover, definitions, references, diagnostics, formatting, and in-memory file updates for QL code.

MCP server that lets AI models invoke CLI agents (Gemini, Codex, Claude, OpenCode) as tools — with parallel execution, retries, and structured output parsing.