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PopNx MCPMCP servers for people who use AI · explained in plain words

AgentPool

by sidduHEREversion 0.1.13i★ –iRuns on your computer
Registry nameio.github.sidduHERE/agentpool

Install

Overview

Description from the maker (original English)

See each coding-agent subscription's live limits and offload work to one with headroom.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "agentpool": {
      "command": "uvx",
      "args": [
        "agentpool-cli",
        "mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add agentpool -- uvx agentpool-cli mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "agentpool": {
      "command": "uvx",
      "args": [
        "agentpool-cli",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "agentpool": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "agentpool-cli",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account sidduHERE, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

  • More stars than 0% of the other 32433 servers in this category
  • Published to the registry more recently than 14% of the other 32433 servers in this category
  • Basic checks equal to or better than 100% of the other 32433 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is AgentPool safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.13) reached the registry 4 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Symantec Cloud DLP AIops

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Spec Driven Development

by formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

Runs on your computer★ 440MITUpdated 14 months ago
Install

Remote Desktop Commander

by desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Runs in the cloud★ 211Updated yesterday
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

AgentPool

You pay for several coding-agent subscriptions — Codex, Claude Code, Cursor, Copilot, Devin, Droid — but you work in one at a time. The rest sit idle until your active provider hits its 5-hour or weekly limit, and then you stall.

AgentPool is a local Python CLI and MCP server that reads the live usage limits of every coding-agent subscription you have and lets you — or your primary agent — offload work to whichever one still has headroom. Use the capacity you already pay for, and keep moving instead of hard-stopping at a cap.

It is a control plane, not an auto-router. AgentPool exposes live provider, model, session, artifact, lease, and best-effort usage/capacity state, and runs the work you offload as explicit worker sessions. You or your agent still choose the provider and model — AgentPool makes the limits visible so that choice is informed, never automatic.

The v0.1 alpha posture is conservative:

  • tmux is the default runtime; Terminal Control is optional when configured.
  • Provider selection is explicit; provider=auto is rejected.
  • Usage/capacity summaries are confidence-tagged and keyed by provider id.
  • CodexBar and ccusage are optional usage helpers when installed or configured.
  • AgentPool does not store provider credentials, scrape browser usage pages, rank models, merge code, or push code.

Requirements

  • Python 3.11 or newer.
  • tmux on PATH.
  • Optional: termctrl on PATH for the Terminal Control runtime.
  • Git for worktree isolation and diff collection.
  • macOS or Linux for live runtimes. Windows is not a v0.1 target except through WSL-like shells.

Install

AgentPool publishes to PyPI as agentpool-cli; the installed command is agentpool.

uv tool install agentpool-cli      # recommended

pipx install agentpool-cli # fallback uvx agentpool-cli --help # zero-install try

Then:

agentpool setup codex

agentpool doctor --deep --privacy

The agentpool-cli package installs on macOS, Linux, and Windows, but live terminal runtimes are supported on macOS or Linux (Windows via WSL).

Optional Terminal Control config:

runtime:

default: tmux terminal_control: enabled: true binary: termctrl session_prefix: agentpool cols: 120 rows: 36

Install from source:

git clone https://github.com/sidduHERE/agentpool.git

cd agentpool uv tool install --force .

Or run from a development checkout:

uv venv

uv pip install -e ".[dev]"

A GitHub release install (wheel pinned to a tag, no PyPI required) is also supported:

scripts/install.sh latest

See docs/install.md for first-run, upgrade, and MCP setup notes.

Quickstart

For AI agents, start by loading the bundled version-matched skill:

agentpool skills get agentpool

agentpool skills get core --full

agentpool init

agentpool setup cursor agentpool config validate agentpool doctor --deep --privacy agentpool setup all agentpool smoke --provider fake-question --repo . --json agentpool inventory --json agentpool usage-summary --refresh --json

That last command is the one you will run most: it shows every configured subscription's remaining limit, reset time, and a usable flag, so you can see at a glance which provider to offload the next task to.

Start an explicitly selected read-only worker:

agentpool spawn \

--provider \ --repo . \ --task "Inspect the project and ask one clarifying question." \ --isolation read_only

agentpool observe --wait-for completed,error,question,approval_prompt --timeout 60 --json agentpool send "Continue with the smallest useful check." agentpool artifacts --json agentpool transcript --tail-lines 80 --json agentpool session show --json agentpool sessions --recent 10 --json agentpool collect --json agentpool terminate --dry-run --json agentpool terminate --json

spawn defaults --initial-prompt-mode to provider_default. For Codex CLI this resolves to arg, which passes the first task as the Codex prompt argument instead of relying on a paste-and-submit startup cycle. Providers that expose reasoning controls also accept process-local overrides such as --reasoning-effort high; Codex also accepts --service-tier priority. AgentPool does not edit your provider config.

For AgentPool-created edit isolation, choose worktrees explicitly:

agentpool spawn \

--provider \ --repo . \ --task "Make the small patch." \ --role implementer \ --isolation worktree

agentpool worktrees list --repo . agentpool worktrees cleanup --session-id --dry-run --json agentpool worktrees cleanup --session-id

Worktree isolation is not forced by default. Users often have their own worktree setup and cleanup rules, so AgentPool only creates a worktree when requested through --isolation worktree or policy configuration.

Usage And Capacity

agentpool usage-summary --refresh --json

agentpool usage-summary --refresh --no-interactive --json agentpool stats --since 7d --json agentpool usage-summary --refresh --backend codexbar --json agentpool usage-summary --refresh --backend ccusage --provider claude-code --json

usage-summary returns a providers object keyed by provider id. It is not ordered and it is not a recommendation list. Each row includes usable, unusable_reason, quota windows, confidence, age/staleness, and reset timing when the provider exposes it. The older CLI capacity-summary command is retained as a human convenience alias; MCP also exposes get_capacity_summary as a compatibility alias for get_usage_summary.

The default buffer is policy.min_remaining_percent = 10. If any reported quota window is below that buffer, the provider row is marked unusable for the summary. Staleness is reported as age information only; it does not by itself make a provider unusable. If you want cached summary reads to refresh automatically after a threshold, set policy.usage_auto_refresh_after_seconds in ~/.agentpool/config.yaml. AgentPool still does not pick an alternative provider for you. MCP usage refreshes are intentionally bounded and may return partial=true; use the CLI commands above when a shell-capable agent needs a complete live refresh. Use --no-interactive or AGENTPOOL_NO_INTERACTIVE_USAGE=1 when a shell script must avoid provider TUI fallback probes.

Provider Matrix

Provider idCommandUsage status in v0.1Model pinning
codex-clicodexnative local app-server rate-limit probe; CodexBar optional--model + config-scoped reasoning/service tier
cursor-cliagent or cursor-agentoptional CodexBar Cursor usage; native CLI usage is interactive /usage only--model + read-only --mode ask
claude-codeclaudetemporary /usage probe; ccusage telemetry optional--model + --effort
devin-clidevinDevin/Windsurf plan-status API from existing CLI auth, with /usage fallback--model
copilot-cligh copilotGitHub Copilot usage API from env or gh auth tokenforwarded --model
droid-clidroidunknown unless surfaced by future safe probeprocess-local settings file + --reasoning-effort
opencodeopencodeconfigured adapter; usage unknown in this alpha--model with provider/model ids

Compatibility note: the PRD calls Factory's coding product factory-droid, but AgentPool exposes it as droid-cli because the installed command is droid. Do not add a duplicate factory-droid inventory row unless a distinct harness appears.

Privacy Posture

AgentPool is local-first, but usage probes can still be sensitive because they read existing CLI auth state and may call provider APIs on explicit refresh.

AgentPool does not:

  • store provider credentials;
  • read browser cookies by default;
  • scrape browser dashboards;
  • trigger login flows;
  • silently accept paid overage.

AgentPool does store:

  • SQLite session, event, usage snapshot, artifact, and lease metadata;
  • transcript and artifact files under ~/.agentpool/artifacts by default;
  • generated runtime settings that are not credentials.

Run:

agentpool doctor --privacy --json

See SECURITY.md and docs/usage-detection.md.

MCP

Start the MCP server:

agentpool mcp

agentpool mcp --toolsets default,stats AGENTPOOL_MCP_LOCKDOWN=1 agentpool mcp

Example host config:

agentpool mcp-config --client generic
{

"mcpServers": { "agentpool": { "command": "agentpool", "args": ["mcp"] } } }

Verified install helpers (deeplink or one-liner shell command):

agentpool mcp-config --client cursor --absolute-command --install

agentpool mcp-config --client claude-code --absolute-command --install agentpool mcp-config --client codex --absolute-command --install agentpool mcp-config --client copilot-cli --absolute-command --install

Raw config generators:

agentpool mcp-config --client claude-code --json

agentpool mcp-config --client codex agentpool mcp-config --client cursor agentpool mcp-config --client claude-desktop --json

Use --absolute-command if the MCP host does not inherit your shell PATH. Verified per-host steps live in docs/mcp-clients.md. Team templates: .cursor/mcp.json.example, .mcp.json.example, and docs/examples/README.md. MCP Registry metadata: server.json. It advertises the agentpool-cli PyPI package and should be bumped with each release. Release checklist: docs/release.md. Provider setup guides: Cursor, Cursor Agent CLI, Codex, Claude Code, Copilot, Droid, and Devin.

MCP-connected agents should read these once on connect:

  • agentpool://onboarding
  • agentpool://skill.md
  • agentpool://preferences.md

Then use tools for live operations. The user-owned preferences file also shows up through agentpool preferences and get_delegation_preferences(). It may say to use your native subagent system instead of AgentPool for some tasks. The default MCP toolset is deliberately small: inventory, usage snapshot, usage summary, provider models, preferences, spawn, observe, poll, send, interrupt, collect, artifact manifest, transcript paging, and terminate. Add opt-in toolsets with agentpool mcp --toolsets default,stats,sessions,leases,worktrees.

Shell-capable agents can use agentpool skills get agentpool instead of MCP resources to load the same core usage guidance from the installed CLI.

Coding agents with shell access should prefer the CLI path. It is more token-efficient because large worker output stays in artifact files and observe/collect return compact manifests by default. MCP remains first-class for MCP-native hosts and no-shell environments. See docs/agent-cli-and-mcp.md.

Development Checks

Development and CI checks are documented in CONTRIBUTING.md.

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