MCP for SaaS: How to Build an AI Agent Distribution Channel

An MCP server can give a connected AI application a standard way to inspect and use your SaaS product. Turning that access into a distribution channel also requires a place where buyers encounter the integration, a useful task the agent can complete, and a measurable path to activation. Publishing a server alone does not supply those elements.

A founder can spend weeks making an integration easy for developers and still leave the commercial opportunity undefined. The API works. The documentation is accurate. Nobody has decided which partner will put the capability in front of a customer at the moment it becomes useful.

AI agents make that distribution problem more specific. You need to identify the workflow where an agent needs your product, the company that controls that workflow, and the action that makes trying your product worthwhile.

That is a partnership brief a technical founder can build against.

Separate product discovery from tool discovery

Model Context Protocol, or MCP, is a standard for connecting AI applications to external tools and information. A server can expose callable tools, readable resources, and reusable prompts. A connected client can list the tools the server makes available and request an execution. The host application determines how those capabilities reach the model and user. MCP server concepts

Three different activities often get compressed into the word “discovery.”

The official MCP Registry provides server metadata for downstream discovery services. Its documentation describes an ecosystem of aggregators and marketplaces; a registry entry does not mean every AI assistant installs, trusts, or recommends the server. MCP Registry

For founders, the practical question is which AI application or implementation partner can bring the capability into an existing customer workflow.

Your website still matters. Rebar’s guide to partner ecosystems and AI search covers public evidence and content discovery. The build here addresses what happens once an application can use your product.

Choose a moment when your product becomes necessary

Consider a fictional SaaS product that helps service companies prepare quotes and schedule site visits.

An implementation agency already builds AI assistants for those companies. During a customer conversation, the assistant identifies a request that needs a formal quote. The agency needs a reliable way to collect the right information and hand the request to a quoting system.

The SaaS company could propose a specific integration: let the agency’s assistant retrieve the fields required for a quote, collect those fields, and prepare a handoff for the customer to approve.

The commercial roles are clear:

• The customer avoids repeating the request in another system.
• The agency gains a reusable component for its implementations.
• The SaaS company gains a product entry point inside the agency’s work.

The agency and SaaS company still need to agree on installation, support, attribution, and any commercial terms. Those decisions belong alongside the technical scope.

Before writing code, complete this sentence:

When a customer needs [specific outcome] inside [partner’s workflow], the agent uses [our capability] to produce [useful result], after [required permission].

If the sentence needs an entire product tour, narrow the use case.

Build one useful, read-only capability

A first MCP experiment should make one task easier to complete. A read-only tool that returns required fields is a reasonable starting point because it can demonstrate the handoff without creating accounts, sending messages, or changing customer records.

The following original example returns fictional workflow requirements. It uses the official Python SDK’s v2 interface. The version range makes that dependency explicit; lock the resolved version when turning the example into a maintained project. Official Python SDK

Create a project and install the dependency:

uv init handoff-demo
cd handoff-demo
uv add 'mcp[cli]>=2,<3'

Save this as server.py:

from typing import Literal
from mcp.server import MCPServer

server = MCPServer("Service Handoff Demo")

REQUIREMENTS = {
    "quote": ["service_type", "scope_summary", "postal_code"],
    "site_visit": ["site_address", "visit_purpose", "preferred_date"],
}

@server.tool()
def get_handoff_requirements(
    workflow: Literal["quote", "site_visit"],
) -> dict:
    """Return fields needed to prepare a fictional service handoff.

    Use when a user wants to prepare a quote or site-visit request.
    Reads a sample template. Does not submit a request or book a visit.
    """
    return {
        "mode": "example",
        "workflow": workflow,
        "required_fields": REQUIREMENTS[workflow],
        "next_step": "Collect missing fields and request user review.",
        "request_created": False,
    }

if __name__ == "__main__":
    server.run(transport="stdio")

Then open the development inspector:

uv run mcp dev server.py

The inspector requires Node’s npx to be available. Inspect the tool and call it with {"workflow":"quote"}. The result should identify the three required fields and explicitly say that no request was created. The official server tutorial explains local execution and connecting a host application. Build an MCP server

This example deliberately stops at preparation. In an actual product, connect the handler to a versioned source of approved requirements. Return supported inputs, useful failure messages, and the next permitted action. Avoid having the agent guess what your backend accepts.

Make the tool easy to choose for the right task

A tool description is part of your product interface. It should identify the user’s intent, the inputs the tool requires, the result it returns, and the actions it does or does not perform.

“Access our platform” gives an agent little guidance. “Return the fields needed to prepare a service quote; does not submit the quote” gives it a concrete selection rule.

Use a small evaluation set before adding more tools:

Run the requests in the intended host application. Inspect the tool selection and final answer. A successful tool response is insufficient if the assistant then tells the user it booked an appointment.

Record the host, model where available, server version, request, selected tool, and outcome. These records let you distinguish an integration defect from an unclear description or a change in agent behavior.

Connect the prototype to a real distribution surface

Local execution proves the interface. A remote partner integration needs an additional deployment and access plan.

Choose a host supported by the intended client, expose the server through the client’s supported remote transport, and document installation. Before exposing customer information or actions, implement the required authentication and authorization. MCP’s authorization guidance covers protected HTTP servers; connecting a client does not grant unrestricted access to a customer’s account. MCP authorization guide

For the fictional quoting product, the first distribution surface could be the implementation agency’s standard service-business assistant. The useful deliverable would include a working connection guide, the read-only tool, a sample customer request, and a recorded handoff through the agency’s actual interface.

Agree on where the customer sees the offer. An integration hidden in a settings menu has different exposure from a quoting option inside an active conversation. The difference should appear in the partner proposal and the measurement plan.

Start with one willing partner and one repeatable customer task. A server directory can support discovery, but it should not substitute for a partner with a reason to deploy the capability.

Measure the path from agent activity to customer value

Count distinct organizations through a defined sequence: connection, first successful task, product activation, and paid conversion where applicable.

Tool calls are an operational metric. They can include retries, testing, or repeated activity from one customer. They should not be reported as leads.

Keep two attribution fields separate. One records where the customer came from. The other records whether an agent-assisted workflow helped the customer activate. An existing customer who begins using an MCP tool belongs in adoption reporting, even if the activity creates no new acquisition.

Where referral credit applies, attach the partner identity through a verified server-side mapping. A model-generated partner name is not reliable evidence of entitlement.

The first commercial review should answer three questions: Did the partner expose the capability to relevant customers? Did those customers complete a useful task? Did completing the task lead to product adoption?

Each answer points to a different next investment.

Common questions

Does an MCP server make my SaaS discoverable by every AI assistant?

No. A compatible application needs a way to find, connect to, and permit use of the server. Tool discovery happens within that connection. Public availability alone does not guarantee adoption or recommendation.

Does MCP replace SEO or answer engine optimization?

No. Public content helps people and retrieval systems understand your product. MCP provides an interface for connected applications. A company may need both, depending on how buyers discover and use its product.

Is an MCP integration the same as agentic commerce?

An MCP integration can support an agent-assisted commercial workflow. It does not, by itself, implement purchasing, payment authorization, contract acceptance, or settlement. Define those responsibilities separately if the proposed workflow includes them.

What should a founder build first?

Start with a capability requested by a specific customer or prospective partner. Build a narrow tool, test it in the intended application, and measure whether the resulting workflow helps a customer activate.

Find a partner with a reason to use the integration

Before funding the larger build, identify the companies that already control the relevant customer workflow. Rebar’s partnership strategy map identifies a priority opportunity, a suitable partner model, a focused target list, and a first move to test the idea.

Request your partnership strategy map. Include your product URL and the workflow you want an AI agent to support in the growth-priority field.