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14 days · $1,999 · fixed

Ship It Live · MCP sprint

Your product, usable by AI assistants, in 14 days.

The integration surface your customers will expect within a year. Built properly, with tools an assistant can actually use without guessing.

4.93★ / 32 reviews 75+ shipped Kickoff in 24h

Quick answer

A Model Context Protocol server exposing your product to Claude and other AI clients can be designed, built and deployed in 14 days for a fixed $1,999. That covers tool and resource design, authentication, error handling, documentation and deployment. Well-designed tools matter more than the number of them.

The plan

Day one to day 14, written down.

You get this schedule before you commit, not after. If a phase slips, that is mine to absorb — the price and the date were agreed before anything started.

  1. 1

    Days 1–2

    Tool design

    Which operations to expose and how to describe them. This is the whole job — a badly named tool with a vague description is one an assistant will misuse confidently.

  2. 2

    Days 3–6

    Build the core

    Tools, schemas, validation and error paths. Errors are written to tell a model what to do differently, not just that something failed.

  3. 3

    Days 7–10

    Auth and hardening

    Authentication, scoping and rate limits. An MCP server is an API surface with an unusually creative client, so the limits matter.

  4. 4

    Days 11–13

    Test with real clients

    Driven from an actual assistant against real tasks. Tools that read well on paper often fail the moment a model has to choose between them.

  5. 5

    Day 14

    Ship

    Deployed, documented, with install instructions and a short handover on adding tools yourself.

What $1,999 gets you

Everything needed to launch — nothing padding the invoice.

Tool design — the part that decides whether an assistant uses it correctly
Resources and prompts where they fit the protocol better than tools do
Authentication, including OAuth where your product needs it
Input validation and error messages written for a model to recover from
Rate limiting and scoping, so a confused agent cannot cause damage
Documentation and install instructions your users can follow unaided
Deployed and running, local and remote transport as appropriate
Source code and configuration yours at handover

Who books this

Built for

SaaS products whose customers already ask about AI integration

API-first companies who want to be reachable from inside assistants

Internal platform teams giving their own agents safe access to systems

The honest part

When 14 days is the wrong answer.

A fixed deadline only works when the scope genuinely fits inside it. These are the cases where I will tell you so rather than take the booking.

Exposing destructive operations without confirmation. Delete, refund and send are gated or excluded — an assistant misreading a request should not be able to be catastrophic.

Wrapping an entire API surface tool-for-tool. Fifty tools is worse than eight; models choose badly from long lists, and I will argue for the shorter one.

Products with no API and no data model yet. That is the project to do first, and this sits on top of it.

Questions

Before you book

A standard way to expose your product's capabilities to AI assistants, so a model can use your tools directly instead of a user copying data between windows. The Model Context Protocol is the interface, and a server is your side of it.

14 days from now, this could be live.

A 30-minute call decides whether the scope fits. If it doesn't, I'll tell you what would.