"ClawdBot Worker - Bot or Worker Service Information" matching MCP servers:

MCP server that exposes tools to search and list GitHub repositories synced to SQLite, enabling natural language queries for top repos by stars or by name.

MCP server for local AGY worker runtime, enabling controlled task execution with permission checks, evidence collection, and status tracking for build, browser, and image workflows.

Delegate coding tasks to external AI coding agents in isolated git worktrees with independent verification, enabling any MCP client to orchestrate multi-agent workflows.

MCP server that enables a coordinator AI agent to spawn, control, and supervise local coding agents with interactive gating for high-risk operations.

A universal Worker control plane exposed over MCP that lets a host configure OpenAI-compatible providers and API keys, discover model capabilities, and run, monitor, steer, or cancel Codex worker threads. It ships an app-only control panel so humans manage secrets and worker permissions while models call worker lifecycle tools.

Enables Codex to delegate bulk code reading, patching, and testing to an async worker using cheaper AI models, while receiving compact results.

Enables a Claude Desktop or Claude Code session to start, drive, and supervise interactive Claude Code CLI worker runs, including sending prompts, reading terminal output, waiting for completion, stopping runs, and managing settings and permissions.

Enables Claude Code to offload bulk reading and first drafts to a local Ollama model on your GPU, and to draft weekly devlogs from merged pull requests.

Enables Codex to offload expensive code reading, editing, and checking to a worker agent via Claude Code, supporting async jobs and long-running tasks.

Enables private, GPU-backed image upscaling via a local Hermes MCP bridge that submits jobs to an authenticated Upscayl worker API.

A Windows-first, dual-mode MCP bridge that delegates bounded work to Hermes Agent and a local Qwen model via an OpenAI-compatible endpoint, providing restricted batch or trusted full execution modes.

Enables bounded, local coding tasks to be delegated to OpenAI-compatible models with explicit working directory and read/write paths, pre-application unified-diff validation, and optional verification commands. It routes tasks by complexity and risk, while blocking sensitive changes such as auth, payment, secrets, migrations, database, deployment, or production environments.

Lets coding agents delegate bounded background tasks to OMP models via an MCP server, with job control, custom roles and agents.

Enables Codex to supervise one or more local Pi coding-agent workers through bounded task execution, state polling, steering, aborting, and listing. Tasks can run in a direct working directory or isolated Git worktree while Codex remains the planner and reviewer.

Enables STDIO-compatible AI assistants to queue coding work onto the official Google Antigravity CLI and track status, read reports or patches, query capabilities, or cancel running tasks without blocking. Supports trusted direct execution plus optional isolated or read-only modes depending on the platform.

Spawn headless OpenCode workers on cheap models to delegate expensive tool-using tasks to a fleet of parallel workers with isolated git worktrees.

Enables delegating asynchronous coding tasks and DAG workflows to local Oh My Pi (OMP) CLI sub-agents, with topological orchestration, path isolation, and supervised resumption.

Enables MCP clients to run Google Antigravity CLI (agy) as durable, detached asynchronous jobs with a permission gate and broker-computed verdicts that do not trust agy's own exit code.

A local-first MCP server that handles daily work tasks through your AI assistant: converts meeting notes into todos, manages todo lifecycle, tracks work hours, generates daily/weekly reports, organizes files via move-only operations, and diagnoses dev environments, all guarded by a human-maintained preview/apply safety model.

Delegates heavy, repetitive, and verifiable tasks like PDF extraction, code analysis, and log processing to a local LLM to reduce token consumption for frontier AI models, while keeping decision-making with the main AI.