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Lazy Ants · AI · MCP & Integrations

MCP Server & LLM IntegrationYour product, usable by Claude, ChatGPT and the agents built on them.

For companies with a product or an API that agents should be able to use, and that should earn from it. An MCP server turns your API into tools an agent can call, with the rights each tool needs, authentication and metering. The same layer connects Claude or OpenAI into your own product. Three packages, each with a fixed price, on this page. Three public MCP servers to read before you call.
An agent in Claude · your MCP server4 · The call is metered and billed
agentWhich Alpine invoices are overdue?
tool call · your MCP serverinvoices.list(client: "Alpine", status: "overdue")
api.yourproduct.com
InvoiceClientStatus
INV-2291AlpineOverdue · 12 d
INV-2305AlpineOverdue · 3 d
INV-2310FjordPaid
Read only · the rights of acct-7f2
2 results returned · 1 call metered · billed to acct-7f2

Illustration · one tool call, end to end

01 · Three packages

MCP server development cost: three packages, each with a fixed price.

Each package has a fixed scope. If your product needs more than the package covers, it moves to the next one, not into an extra charge. Each package becomes a fixed price with the written definition of ready, given on the first call.

1Start here

Agent-Readiness Assessment

Should agents be a channel for your product?

  • Which endpoints become tools
  • What each tool may read and write
  • What agent usage would earn
  • Scope and fixed price for package 2
1 weekFixed price
Book the assessment
2Your product for agents

MCP Server for your API

Your product, usable and billable by agents.

  • Tools from your API, rights per tool
  • Authentication and metering, every call billed
  • Published for Claude and OpenAI
  • Pilot with one real agent
Fixed priceGiven on the first call
Discuss the MCP server
3AI inside your product

LLM Integration

The model in your product, under your control.

ClaudeOpenAI
  • Prompts and limits your admins change
  • Routing between providers, with fallbacks
  • Evaluation before every release
  • Cost and quality monitoring
Fixed priceGiven on the first call
Discuss LLM integration

Not in any package: endpoints your API lacks, and training a model of your own. Need more than a package covers? It moves to the next one, not into an extra charge.

02 · How it runs

Four steps. A real agent uses it before we widen it.

Every package follows the same four steps. Between the third and the fourth there is one gate: a real agent completes a real process through the server.

1

Map

Your API becomes a tool set an agent can understand.

You get: a signed definition of ready
2

Protect

Rights per tool, authentication, metering. Read apart from write.

You get: every call tied to an account
3

Pilot

Published for Claude and OpenAI. One real agent, one real process.

Gate: the process completesYou get: a server in use
4

Run

Monitoring of calls and cost. Versioned tools. Support while you widen it.

You get: the next tools, same server

What it costs

MCP Server & LLM Integration

Agent-Readiness Assessment1 week
MCP Server for your APIFixed price
LLM IntegrationFixed price

Every price is fixed and given on the first call, with the definition of ready.

What moves the price

  • How many tools, and how many of them write
  • The authentication your API already has, or lacks
  • Whether agents pay per call, per result or on a plan
  • One provider, or routing between several

Which package fits is answered on the same call.

What it costs

MCP Server & LLM Integration

Agent-Readiness Assessment1 week
MCP Server for your APIFixed price
LLM IntegrationFixed price

Every price is fixed and given on the first call, with the definition of ready.

What moves the price

  • How many tools, and how many of them write
  • The authentication your API already has, or lacks
  • Whether agents pay per call, per result or on a plan
  • One provider, or routing between several

Which package fits is answered on the same call.

03 · Proof

The MCP servers you can read.

Every agency that offers MCP server development describes it. These are repositories and production systems you can open: three public MCP servers, one running inside a payment platform, and the partner programme behind them.

github.com/lazyants · open source

Hetzner MCP Server

An MCP server for Hetzner infrastructure: an agent that lists, creates and manages servers within the rights it was given.

View repository →github.com/lazyants · open source

Lexware MCP Server

An MCP server for Lexware business operations: invoices, contacts and bookkeeping records reachable by an agent, with the permissions the API already enforces.

View repository →github.com/lazyants · open source

Transkribus MCP Server

An MCP server for document and transcription workflows: an agent that uploads, transcribes and reads documents through the platform's API.

View repository →In production

An MCP server inside CPAY

The crypto payment platform built and operated by our team exposes an MCP server, so agents work with payments through the same rights the API gives a person.

Read the case study →Partner program

Claude Partner Network

Lazy Ants is part of the Claude Partner Network, Anthropic's partner program for organizations helping businesses adopt Claude.

Learn about the Claude Partner Network →Related · agents that pay

Agentic Payments

When agents should pay for your product in stablecoins as well as on a plan, the x402 wallet work is on its own page.

See Agentic Payments →
04 · Scope

Providers, protocol and your API. Where our part ends.

Scope is measured in tools, in the rights each one carries, and in the providers the server is published for. All three are decided in the definition of ready, before anything is built.

Providers and protocol

Claude, OpenAI, and the open MCP standard

The Model Context Protocol is the open standard through which Claude, ChatGPT and other agents call external tools. One server serves them all; it is published in a public registry or kept private to your customers. For LLM integration, we route between Claude and OpenAI and the providers you already use.

Your API

REST or GraphQL, with the authentication it already has

The server sits on the API your product already exposes and the accounts it already knows. Where the API lacks authentication or rate limits, that is listed in the definition of ready before the price is fixed.

Where our part ends

We do not add endpoints your API lacks.Tools are built from what the API can already do. A missing endpoint is product development, scoped separately.
We do not train a model of your own.LLM integration runs on providers' models through one layer you control. Fine-tuning is a separate conversation.
Your data rights decide what a tool may return.What may leave your systems, to which agent and under which terms, is decided with your legal side before step two. How we handle data rights and privacy.
A tool never has more rights than the account calling it.No new rights are created for an agent. If the account could not do it through your UI, the agent cannot do it through the server.

MCP server questions, answered

What product teams ask before exposing what they sell to agents. If yours is not here, ask on the call.

What is an MCP server?
The Model Context Protocol is the open standard through which Claude, ChatGPT and other agents call external tools. An MCP server for your product turns your API into tools an agent can use: listed, described, with the rights each one needs. Without it, an agent can read about your product. With it, an agent can use your product, and you decide what that costs. Our three public servers show what that looks like in code.
Why would my product need one?
Because your customers are starting to work through agents, and an agent can only use what has tools. A product with an MCP server is one an agent can book, query, update or pay for on a customer's behalf. A product without one is one the agent describes and then sends the customer elsewhere. The Agent-Readiness Assessment tells you in one week whether that channel is worth building for your product.
MCP server, LLM integration, or an AI agent for my team?
An MCP server is for agents outside your company using your product. LLM integration is for a model inside your product, run under your control. If the AI should do work for your own team inside your own systems, with approvals and limits, that is AI Agent Development. If it is one internal process end to end, that is AI Automation for Business. We say on the first call which page you are on.
Can agents pay for using my product?
Yes. Package 2 includes authentication and usage metering, so every tool call is attributed to an account and billed the way you bill people: per call, per result or on a plan. If agents should pay in stablecoins as well, that is the Agentic Payments page.
Public or private?
Both are built the same way. A public server is listed in a registry so any agent can find it, which is right for a product that wants new customers through agents. A private server is reachable only by your own customers' agents, with the accounts they already have. Many products start private and go public once the metering has run for a while.
Which providers do you integrate?
Claude and OpenAI first, through the open MCP standard for servers and through their APIs for LLM integration, and the providers you already use. We are part of the Claude Partner Network, which is the one we know best.
Where does my data go?
Only where your data rights allow. Which records a tool may return, to which agent and under which terms is decided with your legal side before anything is connected. A tool returns only what the calling account could see through your UI, and nothing is used to train a provider's model unless you decide so.
How do you keep the tools safe?
Three rules. Each tool has its own rights, and read is separated from write. A write that matters, such as a payment or a deletion, needs a person to confirm. Every call is tied to an account, counted and limited, so a runaway agent stops at the limit rather than at your invoice. All three are tested against a real agent before the server is published.
What does it cost, and how long does it take?
Three packages: the Agent-Readiness Assessment in one week, the MCP server for your API, and LLM integration into your product. Each becomes a fixed price with the written definition of ready, given on the first call. What moves the price and the timeline is how many tools write, whether metering has to be built from nothing, and whether one provider or several are routed.

Tell us about your product and its API

What your product does, who uses it, and what an agent should be able to do with it. If a package is the wrong shape for it, we will say so on the first call.

MCP Server Development | Claude & OpenAI Integration