---
type: "Service"
title: "MCP Server Development"
description: "MCP servers for Claude, ChatGPT and other AI clients"
resource: "https://www.contextstudios.ai/services/mcp-server-development"
language: "en"
generated:
  by: "process:contextstudios-md/1"
  at: "2026-10-08T19:44:35.899Z"
status: "stable"
---

# MCP Server Development

MCP servers built to the current specification: tool design, Streamable HTTP, OAuth 2.1 with PKCE, testing and operations – drawn from running our own production server.

With the 2026-07-28 specification, MCP became stateless: the initialize handshake and the session ID are gone, every request carries its own protocol version and client capabilities, and servers describe themselves via server/discover. That lets MCP servers scale behind an ordinary load balancer. Questions back to the user go through Multi Round-Trip Requests, long-running work through the Tasks extension, interactive interfaces through MCP Apps. For client registration, the specification recommends Client ID Metadata Documents over Dynamic Client Registration. We build your server towards this state without locking out clients on older protocol versions. Our own server at mcp.contextstudios.ai runs in production on protocol 2025-11-25 and already implements parts of 2026-07-28 – no sessions, server/discover, deterministic tool ordering and cache hints. We are migrating the remaining points step by step, and we guide you through the same migration.

## Perfect For



For companies that want to connect Claude, ChatGPT, Cursor or their own agents to internal systems, databases and business applications – with tightly scoped permissions instead of full access. Also for teams that already run an MCP server and want to move it to the 2026-07-28 specification. And for companies that want AI assistants to pull their services, products and facts straight from the source – as they can through our own public endpoint.

## Benefits



- Tool design that models use reliably: unambiguous names, precise descriptions, input and output schemas, and annotations such as readOnlyHint and destructiveHint

- Streamable HTTP without sessions – scales behind any load balancer, no shared state

- OAuth 2.1 with PKCE (S256), Protected Resource Metadata per RFC 9728 and scopes per tool group

- Client registration via Client ID Metadata Documents, with Dynamic Client Registration only as a fallback

- Least privilege: separate endpoints for public read access and authenticated write access, explicit confirmation for destructive actions

- Multi-step flows via Multi Round-Trip Requests or server-issued handles instead of hidden sessions

- Long-running jobs via the Tasks extension with polling instead of blocking calls

- Interactive interfaces right in the chat via MCP Apps where plain text is not enough

- Contract tests for tool schemas in CI and compatibility with clients on older protocol versions

- Discoverability via server.json for the MCP Registry; we add a server card under /.well-known once the SEP-2127 draft lands in the specification

- Operations with structured logging, audit trail and OpenTelemetry trace context

- Deployment as a serverless function, container, in your cloud or on-premises – plus stdio for local use

## How we work on it

- Sprint — 4 weeks · fixed price after scoping: Four weeks on one goal, with something that runs at the end.
- Build & Support — after scoping, ongoing · fixed price after scoping; support billed monthly: We build the project out and stay alongside you once it is live.

### Included

- Scoping the tools, resources and prompts the server exposes
- MCP server built to the current specification (stdio or Streamable HTTP)
- Authentication, permission checks and logging of every tool call
- Tests with common MCP clients, e.g. Claude- and GPT-based applications
- Deployment to your infrastructure and documentation

### Not included

- Training or developing a custom AI model
- Rebuilding the connected source systems
- Ongoing operations after the 30 days of bug fixing (optional via Build & Support)

### How it runs

- **W1 — Use cases & tool design**: We define which data and actions an AI agent may reach through the server, and which are explicitly off limits.
- **W2-3 — Development & hardening**: Tools are implemented with permission checks, input validation and logging, and tested against real clients.
- **W4 — Deployment & handover**: Go-live in your environment and documentation for your team; followed by 30 days of bug fixing.