Gotobeat
SaaS, AI agents and MCP
A live music ticketing platform gained AI agents, an MCP server and automated reporting, from one full-stack team.
MCP server development and Claude API integration
We build MCP servers that expose your APIs and data to Claude and any other MCP client, and Claude API apps with streaming, tool use and prompt caching. Deployed to your infrastructure, including AWS Bedrock.
Free 30-minute call. You leave with a written summary of the approach and a sensible first step. No deck, no pitch.
Discovery sprint from $2,500, builds from $6,000
Stack
- Claude API
- Claude Code
- MCP
- TypeScript
- Node.js
- Python/FastAPI
- AWS Bedrock
- Docker
Sound familiar?
Claude can't see your data
Pasting context into a chat window stops working past the first user, and it spreads data you would rather keep in one place.
Every AI integration is a one-off
Without a standard tool layer, each new client or model means rebuilding the same connectors against the same APIs.
Costs and context limits creep up
Long system prompts and large documents get expensive fast when nothing is cached and nothing is budgeted.
What you get
- An MCP server exposing the tools your agents need, and only those
- Claude API integration with streaming, tool use and prompt caching
- Context-window budgeting for long documents and conversations
- Auth and a scope for every tool, with reads and writes separated
- Claude Code workflows for your engineering team
- Tests, deployment and docs your team can extend
How it works
01
Choose the tools
We list what the model should read and do, who may use each tool, and which actions need a human approval. The list is short on purpose.
02
Build the server and the client
The MCP server and the Claude integration ship in small increments, tested against your real data.
03
Deploy and document
We deploy to your infrastructure, through AWS Bedrock if your data cannot leave your account, and leave docs for adding the next tool.
What MCP server development services include
MCP server development services build a small service that exposes your tools and data to AI clients through the Model Context Protocol, an open standard. Ours include choosing which tools to expose, writing each tool's schema and scope, auth, approval steps for writes, tests against your real data, deployment and documentation for adding tools later.
The useful part of an MCP server is the list of tools, and the useful part of the plan is what we leave off it. On the Gotobeat ticketing platform the server sat alongside AI agents and automated reporting pipelines, so the tools were the reads and writes those agents needed and nothing else. A server that exposes your whole API is a security review waiting to happen.
Claude API integration that keeps cost and context under control
A Claude API integration in production needs four things beyond the first call: streaming so users see tokens as they arrive, tool use so the model can act on your systems, prompt caching so repeated system prompts and documents are not billed in full each time, and a context-window budget so long conversations cannot blow past limits.
We built all four into an enterprise knowledge platform over 1,145 hours: streaming chat over Claude and OpenAI, prompt caching on the stable prefix, and a per-request budget split between history, retrieved passages and the answer. The order of the prompt matters for caching, so the plan fixes it early: system instructions and tool definitions first, documents next, the conversation last.
Claude Code fits here too. For engineering teams we set up Claude Code workflows against your repository, with permissions and conventions written down, so the tool speeds up your own developers instead of producing code nobody reviews.
What an MCP server costs and how long it takes
An MCP server or Claude API integration at Tecifo starts with a discovery sprint from $2,500 that produces the written plan and tool list, and a fixed-scope build from $6,000. A first server with a handful of read tools and one approved write is typically planned in one to two weeks and built over three to five.
Those are typical ranges for an illustrative scope; the written plan sets yours. The price moves with the number of tools, whether any of them write, how your existing auth works, and whether the server must run inside your AWS account through Bedrock. Each is a line in the plan you can keep or cut.
When to build an MCP server and when a plain API integration is enough
Build an MCP server when more than one AI client, or more than one model, needs the same tools, or when you want the tool layer owned and versioned separately from any one app. A plain API integration is enough when a single application calls a single model and the tool list is short.
We have built both and we recommend the plain integration more often than you might expect from a page about MCP. The table below is how we decide on the call.
How we control what Claude can see and do
Every tool on the server has its own auth and scope. Reads and writes are separate tools, write tools can require a human approval before they run, and each call is logged with its inputs and outputs. The model never gets a database connection or an API key; it gets a short list of named actions.
If your data cannot leave your cloud, we deploy the server and the model calls inside your AWS account, with Claude served through Bedrock. The tools stay the same; only the endpoint changes.
MCP server vs plain API integration
Both are things we have shipped. The right one depends on how many clients will call the tools and how long the tool list will get.
Who calls the tools
- Plain API integration
- One app, one model
- MCP server
- Claude, Claude Code and any MCP-capable client
Where the tool logic lives
- Plain API integration
- Inside the app that uses it
- MCP server
- In one service, versioned and deployed on its own
Adding a second client
- Plain API integration
- Re-implement the tools
- MCP server
- Point it at the server
Auth and scoping
- Plain API integration
- Whatever the app already has
- MCP server
- Per tool, with reads and writes separated
Write safety
- Plain API integration
- Built per feature
- MCP server
- Approval step built into the server once
Time to first result
- Plain API integration
- Days
- MCP server
- Weeks, because the tool layer is designed rather than improvised
One app, one model and a short tool list: build the plain integration. Two clients or a growing tool list: build the server once.
Questions, including cost and timeline
Tell us what you're building.
Free 30-minute call. You leave with a written summary of the approach and a sensible first step. Prefer writing? Send a brief and we reply by email with questions.
Discovery sprint from $2,500, builds from $6,000
Or email info@tecifo.com with a few times that suit you.Not ready to talk? Take the checklist.
Fifteen checks we run before real users and real data hit an AI product: prompt caching, context budgets, approval steps, row-level security, agent state, traces. We email you the link.