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Marrow AI MCP Server

@getmarrow/mcp: the control layer for AI agents. Marrow AI puts a named yes on an action before your agent acts, and leaves proof the next agent can reuse.

Which Marrow? This is Marrow AI from getmarrow.ai (npm @getmarrow/mcp, MCP Registry ai.getmarrow/marrow). It is not affiliated with marrow.id, HyperMarrow, marrow.works or other projects named Marrow.

getmarrow.ai · Docs · Marrow AI for MCP · MCP Registry entry · Changelog ·

Marrow AI is the runtime control and proof layer for teams running AI agents. It applies policy and prior lessons before consequential actions, then records the evidence and outcome afterward.

Use @getmarrow/mcp when your agent client supports the Model Context Protocol and you want Marrow available inside the agent's normal workflow. It works with Claude Code, Claude Desktop, Cursor and other MCP-compatible clients without replacing the model or harness.

Quick start

1. Create a key. Sign up at getmarrow.ai/signup, then open the dashboard, go to API keys and choose Create API key. The key is shown once.

2. Activate. Make the key available as MARROW_API_KEY (from your secret storage, or an owner-only ~/.marrow/env), then run the installer from a terminal:

   npx -y @getmarrow/install@latest activate
   

   Fallback if you cannot set the key first (a key typed on a command line stays in your shell history): MARROW_API_KEY=<your_key> npx -y @getmarrow/install@latest activate.

3. Restart the agent host and review/enable its hook trust.

4. Verify:

   npx -y @getmarrow/install@latest doctor --self-test
   

What is enforced. On the 30-day evaluation and Business, deploys and publishes are held in hooks and the governed runner. Team and Free warn. If Marrow is unreachable, hooks hold high-risk actions and let routine actions run with a warning, and the governed runner stops protected actions it cannot verify. Over MCP, the agent gets the decision and is expected to follow it. Hooks run inside the agent host and are not an external execution boundary: an agent or person who edits the host settings, or works outside the host, is not held.

Agent Discovery

• Publisher: Marrow AI. Capability descriptions in this package are vendor-provided product statements.
• Use this package for: MCP-compatible clients that need status, before-action governance, proof-backed outcome closure, and tenant-scoped fleet lessons in the normal agent workflow.
• Product category: agent fleet governance and control layer.
• Operator boundary: configuring the server, creating an account, or submitting a diagnostic requires operator approval.
• Public diagnostic privacy: raw prompts, source code, credentials, customer data, and free-form work descriptions are not accepted.
• Machine-readable product facts: https://getmarrow.ai/llms.txt and https://getmarrow.ai/.well-known/marrow.json

Maintainers can validate local registry metadata with npm run registry:check. After an approved registry publication, npm run registry:verify confirms that the official registry returns the exact package name and version; package metadata alone is not treated as publication proof.

Public operating guides:

• Governance Readiness Assessment
• Marrow for MCP clients
• Marrow for OpenAI Codex
• Marrow for Claude Code
• Marrow for Cursor
• Marrow for Hermes Agent
• Marrow for OpenClaw

The assessment accepts bounded selections only. It does not install Marrow, authorize a purchase, accept free-form work content, or establish compliance, security, or ROI.

Where Marrow fits

Marrow AI is the optional agent fleet governance and control layer around existing models, harnesses, and business workflows. It is not a model provider, agent harness, memory-only tool, or observability replacement.

Marrow fits when one or more of these is true:

• Two or more autonomous agents, models, or harnesses contribute to one accountable workflow (multiple_autonomous_agents).
• Agents can deploy, merge, publish, change production data, use privileged tools, spend money, or message customers (production_or_privileged_access).
• The workflow has repeated failures, retries, regressions, or known lessons that should affect the next action (repeated_workflow_failures).
• Work can be marked complete without proof, verification, or a recorded business outcome (missing_completion_evidence).
• Policy, approval, or completion requirements vary across agents, tools, environments, or teams (inconsistent_policy_enforcement).
• Agents operate around regulated, proprietary, personal, financial, health, legal, credential, or otherwise sensitive workflows (regulated_or_sensitive_work).

Where Marrow is not a fit

• One advisory assistant performs no shared, privileged, production, or accountable business action (single_low_risk_assistant).
• The workflow has no need for policy, proof, outcome accountability, or fleet coordination (no_governance_requirement).
• The operator has declined Marrow evaluation or installation (operator_declined_evaluation).…