MCP server design and build
Tools, resources and prompts modelled against your domain, implemented in Python or TypeScript over stdio or HTTP transports.
Give AI assistants a safe, typed door into your systems.
Custom Model Context Protocol servers that expose your data and actions as typed tools — with authentication, scoped permissions and audit logging built in from the start.
What you receive
Overview
The Model Context Protocol is an open standard for connecting AI assistants to real systems. Instead of writing a bespoke integration for every assistant, you build one MCP server and any MCP-capable client can use it.
The hard part is not the protocol. It is deciding exactly which tools and resources to expose, what each is allowed to touch, how errors come back, and how every call is recorded. That design work is most of what we do here.
Technologies
Each choice is made per engagement and written down with its reasoning.
Problems we solve
If one of these describes your situation, this is the right page.
One MCP server replaces repeated per-client glue code, so adding a new assistant is configuration rather than another project.
Tools expose specific, parameterised operations instead of raw access — scoped, validated, and rejected cleanly when the request is out of bounds.
Documents, tickets, schemas and runbooks become addressable MCP resources, so an assistant reads current sources rather than a stale copy.
Every tool call is logged with its caller, arguments, result and duration, so actions are reviewable after the fact.
Capabilities
Concrete capabilities rather than adjectives. Anything not listed here, we will tell you we do not do.
Tools, resources and prompts modelled against your domain, implemented in Python or TypeScript over stdio or HTTP transports.
JSON Schema and Pydantic-validated inputs and outputs, with error responses an assistant can reason about instead of guessing at.
Per-client credentials, least-privilege scopes, read-versus-write separation and rate limits at the server boundary.
Databases, internal APIs, document stores, SaaS platforms and file systems surfaced as safe, purposeful operations.
Structured logs of every call, latency and error metrics, and alerting on unusual patterns.
Configuration for the assistants your team actually uses, plus documentation so other teams can connect safely.
Process
Specific to this service. The wider seven-stage delivery process wraps around it.
We list the questions and actions assistants need, then decide which become tools and which become resources.
Input and output schemas, error taxonomy and permission scopes are written and reviewed before implementation.
Server, adapters and validation built with tests against each tool contract.
Scope boundaries, injection surfaces, secret handling and audit coverage are checked explicitly.
Deployment, client configuration, runbook and a walkthrough for the teams who will depend on it.
Related work
Entries marked as a content placeholder are reserved for projects whose details are not yet published.
Reserved for an MCP case study: internal systems exposed to AI assistants as typed, scoped, audited tools.
Content placeholderEnd-to-end automation of CRM sync, invoice generation, client onboarding, notifications and reporting.
15 hrs/week time saved · Eliminated manual data entry · Real-time team notifications
Engagements
MCP work is quoted per engagement rather than from a price list — scope depends entirely on how many systems are exposed and what each is allowed to do. Discovery produces a fixed written quote before any build begins.
A short, fixed-scope engagement that produces the capability map, tool contracts and a security review of what you are about to expose — useful on its own, and reusable whoever builds it.
One MCP server over one system or domain: tools, resources, auth, tests, audit logging, deployment and handover.
Multiple servers over several systems with a shared auth and logging layer, plus an ongoing arrangement for new tools as your assistants take on more work.
Questions
The questions we are actually asked about mcp server development, answered without hedging.
The Model Context Protocol is an open standard that lets AI assistants connect to external systems through a uniform interface. A server advertises tools (actions the assistant can call) and resources (content it can read); any MCP-capable client can then use them. It replaces one-off integrations with a single contract.
It is as safe as the tools you expose, which is why contract design comes before code. We default to read-only, require explicit justification for every write operation, scope credentials per client, validate all input, and log every call. We also tell you plainly which tools carry risk.
Any MCP-capable client. We configure the ones your team actually uses and document the connection steps so another team can add a client later without our involvement.
If the consumer is an AI assistant and you expect more than one, MCP usually wins — the tool descriptions and schemas are what the model needs to use it correctly. If the consumer is your own frontend, a normal API is simpler. We will say which applies to your case in discovery, including when the answer is 'you do not need this yet'.
Because scope varies more than any other service we offer. One server over one read-only database is a small job; six servers with write access across finance systems is not. Discovery is short, fixed-scope, and produces a written quote.
Related solutions
Triage, extraction, drafting and research steps handled by grounded AI inside your existing processes — scoped narrowly, evaluated honestly, and escalated to people by design.
02Internal tools, dashboards, APIs and platforms for the parts of your business no off-the-shelf product fits — typed, tested and delivered in your own repository.
03The recurring manual steps between your tools — copying records, chasing approvals, rebuilding the same report — rebuilt as reliable automations with error handling and logs.
Or browse all six services.
Tell us where you are now and what needs to be true in three months. You will get a written scope and a fixed quote before anything starts.
Prices shown exclude third-party costs such as hosting, model API usage and advertising spend, which are paid directly by you.