workhorse-ai-mcp

Your AI is the workhorse. You are the orchestrator.

A delegation journal for orchestrator/worker AI workflows: an append-only event log over SQLite (tasks, reports, artifacts, incidents, full-text search) exposed as an MCP server. Zero dependencies — only Node.js >= 22.5 with the built-in node:sqlite.

The journal enforces a simple discipline: DRAFT → DELEGATED → REPORTED → ACCEPTED | REWORK | FAILED, where reported (the worker thinks it is done) is never the same as accepted (the orchestrator verified it).

Quick start

Claude Code — two commands (installs the MCP server and the skill):

claude plugin marketplace add https://github.com/workhorse-ai/workhorse-ai-mcp
claude plugin install workhorse-ai@workhorse-ai

Any other agent — install the skill, then add the MCP server to your agent's MCP config:

npx skills add workhorse-ai/workhorse-ai-mcp

{ "mcpServers": { "workhorse": { "command": "npx", "args": ["-y", "workhorse-ai-mcp"] } } }

Optionally, pin the rule at the project level — copy this into your AGENTS.md / CLAUDE.md:

Delegation goes through the `workhorse` MCP journal. Worker duties:
`search_precedents` before starting, `record_artifact` (progress notes)
along the way, `submit_report` at the end. Never commit and never accept
your own work — acceptance requires the orchestrator's own test run.

Related MCP server: shinobi

Install

Add the server to your .mcp.json:

{
  "mcpServers": {
    "workhorse": {
      "command": "npx",
      "args": ["-y", "workhorse-ai-mcp"]
    }
  }
}

Or run it straight from a checkout:

{
  "mcpServers": {
    "workhorse": {
      "command": "node",
      "args": ["apps/mcp/server.mjs"]
    }
  }
}

Data lives in ~/.workhorse-ai/journal.db (override with WORKHORSE_DB). On first start the server creates the directory and the database itself. sync.json always sits next to the database.

Connect to the cloud (optional)

connect needs only a token — the managed Workhorse AI cloud is the default:

connect { "token": "pln_..." }

Self-hosted (on-premise)

Pass the base URL of your instance; endpoint paths are derived by the server, so a reverse-proxy prefix works as-is:

connect { "url": "https://workhorse.acme.internal", "token": "pln_..." }
connect { "url": "https://tools.acme.com/workhorse", "token": "pln_..." }

The resolved base is stored in sync.json next to the database. To point every run at your instance without passing a URL, set WORKHORSE_CLOUD_URL.

Configure from .mcp.json instead

If you would rather keep the credentials with the rest of your MCP config — no connect call, no sync.json — pass them as environment variables. They take precedence over the file:

{
  "mcpServers": {
    "workhorse": {
      "command": "npx",
      "args": ["-y", "workhorse-ai-mcp"],
      "env": {
        "WORKHORSE_SYNC_URL": "https://app.workhorse-ai.dev",
        "WORKHORSE_SYNC_TOKEN": "pln_..."
      }
    }
  }
}

WORKHORSE_SYNC_URL takes the same base URL as connect. The journal id defaults to <user>-<host>; override it with WORKHORSE_SYNC_JOURNAL_ID when one machine feeds several journals.

Several workspaces at once

One machine, one journal — but projects may belong to different teams. List the targets in sync.json and the journal is pushed to every one of them, each with its own cursor and its own scope:

{
  "targets": [
    { "alias": "acme", "url": "https://wh.acme.internal", "token": "pln_...", "journalId": "kv-mac" },
    { "alias": "lab",  "url": "https://app.workhorse-ai.dev", "token": "pln_...", "journalId": "kv-mac" }
  ]
}

connect { "alias": "lab", "token": "pln_..." } adds a target instead of replacing the config. On a flat config without an alias it overwrites, exactly as before; once a targets list exists it replaces only its own entry — matched by alias, or by url plus journal id — and leaves the neighbours alone. The flat single-target form ({url, token, journalId}) keeps working untouched, and so do the WORKHORSE_SYNC_* variables — they describe one target, so when a targets list is present they are ignored with a line on stderr rather than silently adding a third destination.

A target that is down does not hold up the others: the push reports per target, and a failing one is a line on stderr, never a crash.

Which projects are pushed (sync scope)

The journal is one per machine and holds every project you work on, while a cloud workspace belongs to a team. Bind the projects that may go there:

sync_scope {}                            # what would be pushed, and why
sync_scope { "projects": ["acme-web"] }   # bind these to the connected workspace

sync_scope asks the cloud for the workspace id itself and records it as cloud_workspace_id in the project registry — no manual ids, no SQLite editing. With several targets, sync_scope {} reports each of them and binding names the target by its alias: sync_scope { "target": "acme", "projects": ["acme-web"] }. WORKHORSE_SYNC_PROJECTS="acme-web,acme-api" overrides the registry for one process. With no mapping anywhere the push stays as before — everything goes — and warns about it on every run.

While a scope is active, events that belong to no project (journal-level incidents, _general) and ProjectRegistered stay local. Widening the scope re-pushes from seq 0 so previously filtered events catch up; the cloud drops duplicates by seq. The last scope is remembered per target in sync-state.json next to the database (keyed by workspace id), so widening the scope of one target does not re-push everything to the others.

If your instance uses a certificate from an internal CA, give Node the root certificate — otherwise the TLS handshake fails and connect refuses to write the config:

NODE_EXTRA_CA_CERTS=/etc/ssl/certs/acme-root.pem

Sync is one-way: the journal is pushed up, the cloud never rewrites it. The cursor request is sent with Cache-Control: no-store, so a caching proxy in front of an on-premise instance cannot serve a stale cursor.

Skills

The package ships three skills — pick the one that matches your setup:

Install via the Claude Code plugin (all three come along), or pick one:

npx skills add workhorse-ai/workhorse-ai-mcp --skill workhorse-ai-orchestrator

License

MIT