Platform MCP Server

ProAgentStore Platform

Marketplace for server-powered AI agents. Creators build agent templates, clients subscribe and run them on their own data.

Store: https://proagentstore.online API: https://api.proagentstore.online MCP: https://mcp.proagentstore.online/mcp Console: https://proagentstore.online/console/ GitHub: https://github.com/ProAgentStore Free pair: https://freeagentstore.online

What's in here

platform/
├── packages/sdk/     Internal TypeScript SDK for agents
├── packages/cli/     @proagentstore/cli — init, check, publish, MCP proxy, local runtime
├── packages/browser-runner/ ProAgentStore Playwright + tmux runtime bundled into the CLI
├── workers/api/      Hono API worker (auth, agents, instances, coding, apply, keys, analytics)
├── workers/host/     Marketing site + console + widget
├── workers/mcp/      MCP server for Codex, Claude Code, Cursor, and VS Code
├── store/            Source HTML for all pages
├── skills/           Open Agent Skills source files
├── plugins/          Codex and Claude plugin wrappers
├── agents/           13 catalog agents (10 flagship + job-application-assistant, coder, repo-chat)
└── templates/        Agent scaffolding (worker, cron, api)

Agent types

Quick start

Use an agent

# Try any published agent — no sign-up needed
curl -X POST https://api.proagentstore.online/v1/public/agents/chatbot/try \
  -H "Content-Type: application/json" \
  -d '{"message":"Hello!"}'

Build an agent

npx @proagentstore/cli init my-agent --template worker
cd my-agent
pnpm install && pnpm dev
npx @proagentstore/cli publish

SDK

import { initPro } from '@proagentstore/sdk'

const agent = initPro({ agentId: 'your-id', token: 'your-token' })
const { response } = await agent.chat('Hello!')

Embed widget

<script src="https://proagentstore.online/widget.js"
  data-agent="chatbot" data-theme="dark"></script>

MCP

codex mcp add proagentstore --url https://mcp.proagentstore.online/mcp
codex mcp list
# If the server shows "Not logged in":
codex mcp login proagentstore

claude mcp add --transport http proagentstore https://mcp.proagentstore.online/mcp
claude mcp list

npx mcp-remote https://mcp.proagentstore.online/mcp
npx @proagentstore/cli mcp

MCP has two runtime modes:

• chat_with_agent calls the public trial endpoint. Use it for discovery and smoke tests.
• subscribe_agent creates your private instance, then chat_with_instance runs that instance with your own state, knowledge, and caller-provided AI credentials.

Typical user run:

list_agents -> subscribe_agent -> my_instances -> add_instance_knowledge -> chat_with_instance

The expected response when user-owned AI credentials are missing is:

Add your Cloudflare Workers AI account ID and API token before running this agent.

That means the instance runtime path is working and correctly refusing to bill the platform AI account. See MCP Instance Runtime for the full tool map, live test record, and OAuth troubleshooting.

The full MCP-first developer surface is documented at:

• https://proagentstore.online/docs/mcp/
• MCP
• server.json
• .mcp.json
• AGENTS.md

MCP safety is enforced server-side. OAuth supports read, write, runtime, and destructive scopes; MCP_READ_ONLY=1 forces read-only mode; mutating tools support dry_run where useful; overwrite/destructive tools require exact confirm values; and mcp_audit_log exposes recent MCP write, runtime, dry-run, denied, and destructive events.

Architecture

The current system map, runtime boundaries, data ownership rules, risk assessment, and refactor roadmap live in Architecture.

Browser runtime (pags up)

Browser- and coding-capable agents use PAGS as the control-plane brain and a local ProAgentStore browser runtime (runtimePlane: "pags", Playwright + tmux, bundled into the CLI) as the hands. One public package, one command — no monorepo, no tunnel binary in the default path.

PAGS control plane / MCP / Workflows
  -> task, auth, approval, audit, the LLM brain
ProAgentStore browser runtime (pags up)
  -> Playwright, local files, real browser profile, tmux CLIs
Real browser / real repo
  -> job boards, uploads, receipts, coding sessions

npm i -g @proagentstore/cli
pags login
pags up            # one multiplexed runner for ALL your instances on this machine

pags up is the canonical runner: one process serves every active instance on that machine. It defaults to a WebSocket relay (the runner connects outbound to a per-instance or node-scoped RelayDO — no cloudflared, no public server, no inbound tunnel). Cloud → callRunner() → RelayDO → WebSocket → runner. Cloudflared tunnels remain only as a legacy --tunnel quick|named option.

• Coder can run multiple machines against the same instance at once. Each coding session is pinned to the runner node that owns it; different repos can run on different machines concurrently.
• pags up --force — replace the current relay socket when debugging stale local connections
• pags up --instance <id> — pin to one agent (debug)
• pags up --headless — headless mode

The job-application agent runs on this runtime via the LLM-driven apply pipeline below (not a fixed job.apply_basic task): POST /v1/instances/:id/apply { url, resumePath } starts JobApplyWorkflow, which drives the runtime's /browser/snapshot + /browser/act endpoints. The Coder agent runs its chosen CLI (Claude Code / Codex / Grok) in a tmux pane on the session's assigned runner node.

Job application agent (LLM-driven apply)

The flagship apply flow: a Brain (Cloudflare Workflow JobApplyWorkflow, using the user's BYOK Claude) drives the Hands (the local browser runtime) to fill and submit a real application — snapshot the ARIA tree → pick one action → act → repeat. Durable + resumable (escapes the 30s Worker limit). Retry + attempt tracking per job. Three human-in-the-loop handoffs share one pause/resume machine: captcha (solve in a live takeover, auto-resumes), stuck (do one step + Resume), needs_input (supply a value → saved to Profile → resumes). Per-ATS tips are cached and fed back next run; "Open in Gmail" surfaces confirmation links. dryRun:true fills everything but a workflow-level guard blocks the final Submit click.

Skills and plugins

ProAgentStore publishes skills through platform-specific plugin marketplaces so users can find them from both Codex and Claude Code.

Codex:

codex plugin marketplace add ProAgentStore/platform

Claude Code:

/plugin marketplace add ProAgentStore/platform
/plugin install proagentstore@proagentstore
/reload-plugins

See Skill Publishing for the publishing layout, marketplace files, and dual Codex/Claude release checklist.

Public discovery pages:

• https://proagentstore.online/skills/
• https://proagentstore.online/skills/proagentstore-mcp-operator/
• https://proagentstore.online/llms.txt
• https://proagentstore.online/llms-full.txt
• https://proagentstore.online/skills.json

Catalog agents

13 first-party agents ship in the catalog: 10 flagship + three headliners (job-application-assistant, Coder, Repo Chat).

Other capabilities

• Two-way voice in the Assistant chat and the Coder Co-pilot (shared useVoice hook) — pick Dictation (browser, real-time live words) or Whisper (OpenAI via the key proxy, most accurate) STT; browser or OpenAI TTS with a voice/speed picker; a single segmented Chat · Tap-to-talk · Hands-free mode control (Mute is a Hands-free sub-control; icon-only on mobile); a spoken "repeat" command; and double-tap any voice message to replay its saved recording (R2, per-turn). Adaptive VAD + pause/sensitivity/language settings; iOS gesture handling.
• First-class Markdown documents in Knowledge — create/read/edit; the agent reads and updates them via the Assistant.
• Observability — browser + server errors flow to a durable log (client:voice*, keys-proxy, job-apply, …) surfaced via MCP list_errors; a unified per-run timeline via agent_events + MCP agent_trace.
• Agent-configurable work board — one board per instance; columns are declared per agent (capabilities.boardColumns), one card per job, with move / retry / attempts. Driven from MCP via instance_board. (Replaces the old two-board / "runtime board" design.)

Part of the FreeStore ecosystem

License

MIT