MCP (Model Context Protocol) Server Builds

Overview

What we deliver

We build MCP servers that expose your systems, data, and tools to Claude, Cursor, and other AI clients through a standard protocol.

Model Context Protocol is the emerging standard for connecting AI assistants to external tools, data, and services. We build custom MCP servers that let Claude, Cursor, Windsurf, and other clients securely access your databases, APIs, internal tools, and document stores. Our work covers protocol implementation, resource and tool definitions, authentication flows, permission scoping, error handling, and deployment. We follow the MCP specification closely so your servers work across compliant clients without rework. Typical builds include CRM connectors, internal search tools, deployment helpers, ticketing system bridges, and data warehouse query interfaces. We also harden servers for production use with auth, rate limiting, audit logs, and observability. Whether you need one server for an internal team or a fleet of servers for a product, we plan the architecture, build the code, and document everything so your engineers can extend it. Servers ship as deployable packages.

Fit Check

Built for teams like yours

Who it's for

AI-forward engineering teams
Internal tools teams
Developer experience leaders
SaaS platforms
Enterprise IT

Pain points we solve

AI clients cannot reach internal systems
Custom integrations for every AI tool
No standard auth for AI access
Manual context copying into prompts
Tool sprawl across AI assistants

What's included

Capabilities

Everything we cover in this engagement.

MCP server scaffolding
Resource and tool definitions
OAuth and API key auth
Database and warehouse connectors
Internal API bridges
Permission and scope controls
Audit logging
Client compatibility testing

How we work

Our process

A clear, predictable path from kickoff to outcomes.

01

Use case scoping

We map which systems AI clients need to reach.

02

Server design

We define resources, tools, and auth flows.

03

Build

We implement and test against MCP clients.

04

Hardening

We add auth, rate limits, logging, and error handling.

05

Deployment

We ship the server and document client setup.

What you get

Deliverables & outcomes

What you get

MCP server source code
Deployment configuration
Tool and resource catalog
Client setup guide
Authentication setup
Audit log integration

Outcomes you can expect

AI assistants can act on internal data
Faster developer workflows
Reusable across multiple AI clients
Auditable AI tool use
Less prompt copy-paste

Timeline

2 to 5 weeks

Engagement

Monthly retainer, Project, Sprint

Tools we use

MCP SDK, Node.js, Python, TypeScript, Docker

KPIs we track

Tool call success rate, auth failures, request latency, active clients, audit log completeness

Client stories

What clients say

"

Holiday season was about to break us. We needed 22 agents in six weeks and our internal hiring pipeline could not move that fast. They staffed it, trained on our tone guide, and ran nesting alongside our senior reps. CSAT actually went up by three points during peak. First Q4 in four years my support lead took her vacation.

Tom H.

"

We had been prototyping an AI quoting agent for nine months and could not get it past demo quality. They came in, scoped a real eval set, swapped our retrieval layer, and added guardrails for the edge cases that kept burning us. Went live in seven weeks. It now handles 41 percent of inbound quote requests without a human touching them.

Kyle A.

Proof