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308,112 tools. Last updated 2026-07-28 05:26

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

  • Installs local multi-agent coordination files so AI agents, Xcode, and humans share the same project truth layer. Use once per project to set up .axint/agent, context, and coordination files.
    Apache 2.0
  • Execute predefined queries within CData Sync jobs for testing or ad-hoc operations. Run existing task queries to validate data synchronization workflows and connections.
    MIT
  • Run a local Warp Oz AI agent for coding tasks like refactoring, testing, debugging, or explaining code. Agent can create or modify files in the workspace and uses no cloud credits.
    MIT
  • Analyze complex prompts and split them into sub-tasks with assigned agents and dependency ordering. Returns a structured plan for execution.
    Apache 2.0
  • Install AI customization elements like personas, skills, templates, agents, or memories from the DollhouseMCP collection to your local portfolio for dynamic persona management.
    AGPL 3.0

Matching MCP Servers

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    Exposes Anthropic's computer-use action surface (screenshot, click, move, keyboard, clipboard, batch) against a persistent desktop display via MCP stdio protocol. Enables AI agents to control a virtual desktop environment through natural language instructions.
    Last updated
    24
    MIT

Matching MCP Connectors

  • Search for AI agents by skill on a P2P network. Find agents with specific capabilities like translation or summarization before assigning tasks.
    Apache 2.0
  • Store temporary work data in a local SQLite workspace for later retrieval by AI agents during long-running project tasks. Offload context like API results or file paths.
    Apache 2.0
  • Send coding tasks to OpenAI Codex for code generation, file operations, and technical analysis using the local CLI with cached authentication.
    Apache 2.0
  • Run Python code directly within DaVinci Resolve to automate tasks, manipulate timelines, manage media, and control Fusion operations through script execution.
    MIT
  • Create background processing tasks in Tembo by specifying prompts, repositories, and agents for automated work execution.
    MIT
  • Create, manage, and execute reusable AI skills including LLM prompts, connectors, rules, and guardrails. Supports direct execution of code, browser, and multi-model tasks.
    AGPL 3.0
  • Manage agent lifecycles, spawn specialized sub-agents, run automated code review and repair, orchestrate agent swarms, search persistent memory, and ingest tasks from external sources.
    MIT
  • Access Agno SDK documentation to build AI agents in Python. Guides cover agents, tools, memory, knowledge, teams, and workflows.
    MIT
  • Execute Python code with automatic retry and intelligent error analysis. On failure, it diagnoses the error, searches past solutions, and suggests fixes for robust execution.
    MIT
  • Launch AI agents to complete tasks on virtual computers using natural language instructions. The tool runs tasks asynchronously on Orgo's infrastructure and provides a URL to monitor progress.
    MIT
  • Execute AI coding agents in parallel batches to analyze, refactor, or generate code across multiple files using automated permission handling.
    Apache 2.0