An agent-ready website gives AI assistants reliable public data and safe, explicit ways to act. Raze builds the machine-readable pages, schema, llms.txt, MCP and WebMCP servers, and guarded workflows that turn a site an agent can only summarize into one it can actually use.
Share the website, the product, and the workflow. We identify the useful agent actions and the data, permissions, and safeguards each one needs.
Agent-ready architecture, running in production on our own site before we sell it to yours.
Readable first, operable second. Accurate public data comes before actions — and actions only where there is a real system behind them with defined inputs, permissions, and errors.
Services, pricing, policies, and company facts get semantic structure, schema, stable URLs, and clean markdown responses. The version an agent reads stays in sync with the one people see.
Discovery files declare which capabilities exist, where the tool server lives, what inputs it takes, and which actions need explicit user confirmation before they run.
We wire agent tools to live data and real workflows through the Model Context Protocol, browser-native WebMCP, or a purpose-built API — whichever the client's stack and buyers justify.
Typed validation, scoped access, legible errors, and an explicit confirmation gate in front of anything that books, buys, submits, or otherwise changes the world.
Agents fail on websites for boring reasons: the data is locked in visual components, the action has no tool path, or nothing defines when a human has to say yes.
An assistant can summarize the page and then stop. No supported way to compare options, pull live data, or take the next step.
Prices, availability, and policies exist only inside scripts and interactive components. An agent has to guess, and guessing is how it gets your pricing wrong.
Browser automation breaks the moment a button moves. A typed tool call with predictable errors does not.
Any useful agent workflow has to state what it changes, what it needs, and the exact point a human must confirm. Most have none of that written down.
Publishing a manifest for a connector nobody configured is worse than publishing nothing. The agent tries, fails, and stops trusting the source.
Scope the jobs worth supporting, define the contracts, build the integrations, then test them against the systems an agent will actually hit.
Which public questions and authorized tasks earn a tool, and a hard line between read-only access and anything that changes state.
Canonical fields, tool inputs and outputs, error cases, permissions, confirmation points, and the backend that owns each result.
Structured pages, metadata, manifests, markdown endpoints, MCP or WebMCP tools, API connections, and validation across the approved scope.
Discovery, read paths, malformed inputs, unavailable states, permission boundaries, and confirmed actions — run against live systems, not a mock.
The work is done when discovery points to a capability that exists, the capability returns current data, and every material action enforces its stated confirmation rule. Not before.
What agent-ready infrastructure adds, using this website as the working example.
10 tools
razegrowth.com exposes ten MCP tools at /mcp, from get_pricing and list_services through get_available_slots.
Declared
Capabilities, schemas, and endpoints published so a compatible agent finds them instead of guessing what the site supports.
Typed calls
Real integrations with validated inputs and predictable errors, replacing click simulation that breaks on any layout change.
Confirm first
create_booking only fires after a visitor selects a slot and explicitly confirms. Every state-changing tool works this way.
Most useful for companies with public product or service data and a real workflow that can be exposed through a stable API or tool contract.
For companies that want agents to check services, pricing, availability, and intake requirements — then book through a supported, confirmed path.
For products with stable APIs, public documentation, plan data, or repetitive account tasks that can be exposed safely.
For companies ready to connect agents to live data and enforce the same business rules the human interface already enforces.
“I've worked with a lot of designers. Edin and Mergim are the only ones who never made me trade speed for quality. They shipped across GrowthX and our client work and never once became the bottleneck.”
Jason Gong, VP GTM
GrowthX
“We needed Docket to look like an AI company you'd trust, not another startup with a gradient and a promise. Raze got us there fast, stayed direct, and never let the project wander.”
Arun Lal, SVP of Marketing
Docket AI
“Edin and Mergim ran Emotive's marketing site for years. Not a one-off project, the actual site. It always looked a step ahead of where the company actually was, which is exactly what you want your website doing.”
Brian Zatulove, CEO
Emotive
“Raze rebuilt our site and it was the rare project where I never had to chase anyone. They understood the technical side, sweated the details, and shipped before I expected it.”
Ty Magnin, CEO
Animalz
The things every client asks us on the first call.
An agent-ready website gives AI assistants structured public information and supported ways to complete approved tasks. It typically combines semantic HTML, schema markup, llms.txt, markdown endpoints, capability manifests, MCP or WebMCP tools, input validation, permissions, and explicit confirmation before any state-changing action.
The Model Context Protocol is an open standard for connecting AI applications to external tools and data through defined schemas. An MCP server can expose read-only information, actions, or both. The client still has to support the protocol and connect to that server — publishing one does not automatically reach every AI assistant.
WebMCP lets a supporting browser page register structured tools for an AI agent inside the active browsing context. It exposes page-aware navigation, data, forms, and actions directly, rather than making the agent infer everything from the rendered interface and simulated clicks.
No. llms.txt helps an AI system find concise public context, but it creates no tools, no live data, and no permissions. Full agent readiness usually also requires structured content, stable APIs, capability discovery, tool schemas, backend integrations, and safeguards on any action that changes state.
Yes, when the site exposes a real integration and the action follows explicit authorization rules. On razegrowth.com, an agent can call get_available_slots to read live Cal.com availability, but create_booking only executes after the visitor selects a specific slot and confirms it.
No. The same site still has to work well for people. Agent-ready infrastructure sits alongside and beneath the interface — structured content, discovery files, and tool endpoints — without changing the visual experience a human visitor gets.
No. A website can publish discovery information and expose tools, but each AI client must support the protocol and be connected or enabled by its user. Raze does not claim direct agent access for a client unless the connector is genuinely configured and working.
Yes. razegrowth.com exposes ten MCP tools at /mcp covering site summary, pricing, services, projects, resources, booking profile, live availability, and confirmation-gated booking. The connection guide is published at /agents.
Tell us what you need. We will review the context and come to the session with a clear recommendation.
Agent-Ready Websites
