Provision

Concept primer · 2026

OpenClaw vs MCP:
they're not the same layer.

The two terms get confused constantly. OpenClaw is an agent harness. MCP is a tool-use protocol. They live at different layers of the stack and they work together. This page explains what each one actually is, where the confusion comes from, and how Provision uses both.

The agent stack — where each one lives

The cleanest way to understand the relationship is to look at the layers from top to bottom. Each row depends on the one below it; nothing on this list replaces another row.

Product

Provision, Claude Code, Cursor, Lindy, Viktor

What the user actually interacts with — a Slack agent, a Discord bot, a dashboard, a CLI.

Harness

OpenClaw, AutoGPT, Hermes

The orchestration loop — plan, act, observe, decide. Memory, browser, filesystem, skills.

Protocol

MCP, OpenAI function calling, Anthropic tool use

How agents talk to tools and data sources — a wire format and capability spec.

Model

GPT-5, Claude Sonnet 4.6, Gemini, Llama

The LLM itself — the reasoning engine.

Infrastructure

AWS, Hetzner, your laptop, Provision Cloud

Servers, browsers, sandboxes, networking — the runtime the harness lives on.

OpenClaw lives at the harness layer. MCP lives at the protocol layer. The harness usesthe protocol to reach tools. They're not in conflict; they're collaborators.

What is OpenClaw?

OpenClaw is an open-source AI agent harness. It sits between the LLM and the world, deciding what the agent should do next and executing each step. It ships with a sandboxed Chrome browser the agent can drive, a filesystem the agent can read and write, a persistent memory store, a skill system, and an execution loop that handles the plan-act-observe-decide cycle.

Critically, OpenClaw is the part that decides. When a user says "research our top 3 competitors and email me the brief," OpenClaw is what plans the steps, executes each one, recovers from failures, and reports back. The LLM is the reasoning engine OpenClaw delegates thinking to.

Provision is a managed cloud and platform layer on top of OpenClaw — adding a managed runtime, a per-agent email inbox, one-click Slack/Telegram/Discord/Web Chat integrations, a dashboard, and a team-of-agents structure. The harness is still OpenClaw; Provision makes it shippable to a real team in five minutes instead of a weekend.

What is MCP?

MCP — Model Context Protocol — is an open protocol for connecting AI agents to external tools and data sources. Anthropic introduced it in late 2024; the spec and implementations are MIT-licensed and open. The core idea is that everyone was building bespoke connectors between agents and the same handful of systems (Slack, GitHub, Notion, Postgres, Jira) — over and over — and the duplicated work was holding the ecosystem back.

MCP defines three primitives. Resources are read-only data the agent can fetch (a customer record, a dashboard snapshot, a file). Tools are functions the agent can call to take action (send an email, create a Linear ticket, query a database). Prompts are parameterized prompt templates a server can offer to clients. An MCP server exposes these capabilities; an MCP client (an agent harness, or a model gateway) consumes them.

Once you've built an MCP server, any MCP-aware agent can speak to it without custom integration code. Build once, use everywhere. That's the bet — and given how quickly the spec has been adopted by Cursor, Continue, Cline, Sourcegraph Cody, and others, it appears to be paying off.

How OpenClaw and MCP work together

The right way to think about it: OpenClaw is the worker, MCP is one of the worker's ways of reaching the world. Concretely:

Step 1

OpenClaw plans a task

An OpenClaw agent receives a request — "draft and send a follow-up to the leads from yesterday's signups." The harness plans the steps: query the CRM, draft an email per lead, send via the agent's inbox.

Step 2

It calls an MCP server for data

Your CRM exposes an MCP server with a get-recent-leads tool. OpenClaw calls it. The MCP server returns lead records in a standard shape.

Step 3

It uses its built-in tools to act

OpenClaw uses its native browser, email inbox, and skills to draft and send. The MCP layer handled the data-source half; the harness handled the action half.

The agent in this story is OpenClaw. The data-fetching is MCP. Neither replaces the other. If you removed MCP, you'd need a custom CRM connector instead. If you removed OpenClaw, you'd have a tool server with no agent to call it.

How Provision uses both

Provision is built on OpenClaw. Every Provision agent is an OpenClaw agent — same harness, same browser-and-filesystem model, same skill system. We add the managed cloud, the email-per-agent layer, the one-click channel integrations, and the team-of-agents UX on top.

MCP fits naturally into that stack. If you've built MCP servers for your internal data (your CRM, your data warehouse, your knowledge base), Provision agents can speak to them through OpenClaw's MCP-aware tool-use layer. You don't re-implement that connector for each agent — the protocol does the deduplication for you.

The other direction is also relevant: Provision's own capabilities (the per-agent inbox, the channel handles, the browser sessions) are increasingly exposed through MCP-style interfaces, so other agents and IDEs can interact with them without custom glue.

When to care about which

Care about MCP when…

  • You're a platform team building data-source connectors and want to write each one once.
  • You're picking which agents/IDEs/tools to standardize on across an org.
  • Your internal stack already has Postgres / Stripe / Linear / Notion / your CRM, and you want agent access without writing N integrations.
  • You want a contract between your data team and your AI/agent teams.

Care about OpenClaw / Provision when…

  • You want AI agents that actually do work — research, drafting, browser tasks, email, channel chat.
  • You need agents in Slack, Telegram, Discord, or your Web Chat — not just on a developer's laptop.
  • You want a managed runtime so you're not building agent infrastructure yourself.
  • Your end users are non-technical and need to chat with the agent like a colleague.

FAQ

Is OpenClaw a competitor to MCP?
No. They operate at different layers of the stack. MCP is a protocol for tool/data-source connectivity that any agent can use. OpenClaw is an agent harness — it orchestrates the loop of plan, act, observe, repeat — and can speak MCP as one way to reach external tools. The right mental model: OpenClaw uses MCP, not OpenClaw vs MCP.
What is MCP exactly?
MCP (Model Context Protocol) is an open protocol introduced by Anthropic in late 2024 for standardizing how LLMs and AI agents connect to external tools and data sources. It defines a JSON-RPC transport, capability negotiation, and a small set of primitives (resources, tools, prompts). Servers expose capabilities; clients (typically agents or model gateways) consume them. The goal is to stop everyone reinventing tool-use connectors.
What is OpenClaw exactly?
OpenClaw is an open-source AI agent harness — the orchestration layer between an LLM and the world. It gives an agent a sandboxed browser, a filesystem, a skill system, persistent memory, and an execution loop. It's the part that decides what to do next, not the part that connects to a specific tool. OpenClaw is what Provision wraps and manages.
Can Provision agents use MCP servers?
Yes. Provision agents run on the OpenClaw harness, which can connect to MCP servers for tool/data-source access. If you've already built MCP servers for internal data, your Provision agents can speak to them. The opposite is also true: Provision exposes its own tools, and over time more of those are reachable via MCP.
Do I need to know MCP to use Provision?
No. Provision is a managed product — agents come with a built-in browser, email inbox, channel integrations, and OpenClaw skills out of the box. You can use Provision without ever touching MCP. MCP becomes useful when you want to connect agents to internal data or proprietary tools through a standard protocol.
Will MCP make agent harnesses obsolete?
No — MCP is plumbing, not a brain. An agent still needs an orchestration loop, memory, planning, and decision-making. MCP makes the connectivity layer cleaner; harnesses like OpenClaw still do the agent work. If anything, MCP makes harnesses more powerful by giving them a standard way to reach external systems.
Is MCP open source?
Yes. The protocol spec is open and the reference implementations are MIT-licensed. Anthropic shepherded the spec; the broader ecosystem (server implementations, clients) is community-driven.

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