Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Descripción del creador (original en inglés)
See each coding-agent subscription's live limits and offload work to one with headroom.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"agentpool": {
"command": "uvx",
"args": [
"agentpool-cli",
"mcp"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add agentpool -- uvx agentpool-cli mcpPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"agentpool": {
"command": "uvx",
"args": [
"agentpool-cli",
"mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"agentpool": {
"type": "stdio",
"command": "uvx",
"args": [
"agentpool-cli",
"mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 6 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub sidduHERE, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (0.1.13) llegó al registro hace 4 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Python (uv) en tu computadora.
En la misma categoría: Spec Driven Development, Remote Desktop Commander, Dechonet MCP
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
30 security tools for AI agents: phishing links, exposure maps, DNS, SSL, PQC, history, pivots.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
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:
provider=auto is rejected.PATH.termctrl on PATH for the Terminal Control runtime.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.
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.
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 id | Command | Usage status in v0.1 | Model pinning |
|---|---|---|---|
codex-cli | codex | native local app-server rate-limit probe; CodexBar optional | --model + config-scoped reasoning/service tier |
cursor-cli | agent or cursor-agent | optional CodexBar Cursor usage; native CLI usage is interactive /usage only | --model + read-only --mode ask |
claude-code | claude | temporary /usage probe; ccusage telemetry optional | --model + --effort |
devin-cli | devin | Devin/Windsurf plan-status API from existing CLI auth, with /usage fallback | --model |
copilot-cli | gh copilot | GitHub Copilot usage API from env or gh auth token | forwarded --model |
droid-cli | droid | unknown unless surfaced by future safe probe | process-local settings file + --reasoning-effort |
opencode | opencode | configured 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.
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:
AgentPool does store:
~/.agentpool/artifacts by default;Run:
agentpool doctor --privacy --json
See SECURITY.md and docs/usage-detection.md.
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://onboardingagentpool://skill.mdagentpool://preferences.mdThen 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 and CI checks are documented in CONTRIBUTING.md.