Let an AI assistant call your business without letting it run it.

Give AI assistants defined tools to query your catalogue, check availability or start a booking. Each tool has an access scope, rate limit and activity log. Our own public server lets assistants read our services, pricing and case studies.

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Who it is for

Teams that need this workflow.

Businesses whose customers already ask an AI assistant before they search

Product companies that want their data queried by agents on their terms rather than scraped

Teams that need internal tools an assistant can call safely: read a price, open a ticket, check a slot

What it does

MCP servers and agent-callable surfaces, by the job.

Expose

Exposes bounded tools

Each tool does one thing an assistant should be allowed to do: read a price, check a slot, quote a job, open a ticket. Nothing an assistant should not do exists to be called.

Describe

Describes the business to machines

llms.txt, structured data, a WebMCP surface on the site and a public MCP endpoint, so an assistant finds the right tool instead of guessing from a page.

Guard

Guards every call

Authentication where needed, rate limits, input validation, and a log of who called what with which arguments. An assistant's mistake is contained to one bounded action.

Convert

Converts assistant traffic

A quote returned to an assistant carries a link a person can complete. The assistant did the finding. Your funnel still does the closing.

How it is built

The build, in order.

The pilot covers one defined workflow. We agree its success metric and kill threshold in writing before the build; the steps below show how this system is developed and evaluated.

Choose the first tools

The three or four questions an assistant is most likely to ask about you, read from the API you already have. Read tools first, always.

Build and publish the server

An MCP endpoint with a tool list, input schemas, rate limits and logs, plus the site-side surfaces that let assistants discover it.

Watch what gets called

The log of real assistant calls is the product research no specification gives you. Tools nobody calls are removed. Tools that get called with the wrong arguments get better schemas.

Add write actions last

Bookings, quotes and tickets that create obligations are added last, with authentication, a check on every input, and the customer confirming anything irreversible.

In production at Strataigize

We run this ourselves.

mcp.strataigize.com is our own public MCP server: an AI agent can query our services, pricing and case studies directly. This site also publishes llms.txt, a full-text index for retrieval systems, and a WebMCP surface. Running one is how we learned which tools an assistant actually calls, which is the part no specification tells you.

Should your business be agent-callable?

Governance

Controls and human review.

Each tool is scoped to one bounded action, and anything irreversible needs the customer's own confirmation
Every call is validated, rate-limited and logged with its caller and its arguments
Tools can be withdrawn in minutes, and the server can require authentication per tool

The rest of the posture is published on trust and security: client-granted access you can revoke, credentials in managed stores, an AI governance summary aligned to the NIST AI Risk Management Framework, our subprocessors, and a DPA on request. We do not hold SOC 2 and we say so there.

Questions

MCP servers and agent-callable surfaces, answered.

What is an MCP server?

A server that speaks the Model Context Protocol, the open standard AI assistants use to call external tools. It publishes a list of tools with their inputs, and an assistant such as Claude or ChatGPT can call them during a conversation. For a business it is the difference between an assistant reading about you and an assistant acting with you.

Is it safe to let AI agents call our systems?

We scope each tool to one action, validate inputs, rate-limit and log calls, and require the customer's confirmation for anything irreversible. The review also identifies which systems and actions should not be exposed. These controls define the permitted use; they are not a claim that every integration is risk-free.

How long does MCP server development take?

A first server with a handful of read tools ships in two to six weeks on top of an API you already have. Adding write actions, authentication and a public listing takes about the same again. If you take an audit, it ranks which tools to expose first.