Model Context Protocol.The open standard that gives LLMs real tools.
MCP is the protocol that turns an LLM into an agent. It defines how a model discovers tools, calls them, streams results back, and authenticates against your systems. With 1.0 ratified and a remote-server registry of 500+, this is no longer an experimental layer.
What MCP solves
Before MCP, every team invented their own tool-call protocol. After MCP, you write a server once and any compliant model client can use it — Claude, GPT, local models. The wins compound: one shared auth profile, one discovery handshake, one schema format.
When to build your own server
Build an MCP server when you have a domain-specific tool that needs to be reused across agents — ticketing, billing, internal search. Use a public server when the integration already exists and is curated. Skip MCP entirely for one-off scripts; the protocol overhead is real if you only ever call one tool once.
The cost of an over-loaded registry
Every registered tool adds ~8–12 KB of schema overhead to every API call, plus selection noise that hurts accuracy. Audit your registry: anything not called in the last 30 days, drop from the default load.
Deep dives on Model Context Protocol (MCP)
MCP 1.0 is here. What changes for the servers you already wrote
The protocol stabilised. Most working servers will keep working. Three places the new spec actually requires changes — auth profile, server registry, streaming-response semantics — with diffs from a real migration.
Why every team's first MCP server should be "list-files"
Smallest useful server. Hardest one to mess up. Teaches the protocol without distracting domain logic. The 60-line server we hand to teams during training.
Visual breakdowns on Model Context Protocol (MCP)
Latest in Model Context Protocol (MCP)
GitHub cuts agentic CI workflow costs 19-62% by pruning tools and moving data-fetch outside the LLM loop
MCP 1.0 ratified — official SDKs in Python, TypeScript, Go, Rust, Java, .NET
How Model Context Protocol (MCP) ships in our engagements
The pages below are the buyer-focused, conversion-grade versions of this topic — deliverables, methodology, ROI, security considerations, and CTAs to scope a real engagement.
Agentic AI Consulting
Designed, built, and handed off — production agentic systems for enterprise teams.
Explore the Agentic AI Consulting solutionMCP Integration
Custom Model Context Protocol servers that turn your systems into agent tools.
Explore the MCP Integration solutionAI Systems Engineering Training
Eight-day corporate training programs that take dev teams from AI-assisted coding to production agentic systems.
Explore the AI Systems Engineering Training solutionEnterprise AI Architecture
Reference architectures for organisations standing up an AI platform — not one agent, but the foundation for many.
Explore the Enterprise AI Architecture solutionAI Automation for Enterprises
Operational agents that replace manual workflows — triage, support, ERP integration, content pipelines.
Explore the AI Automation for Enterprises solutionModel Context Protocol (MCP) — the questions teams actually ask
Train your team on Model Context Protocol (MCP)
Two tracks — one for developers who build agents, one for business teams who use them. Customised to your stack, hands-on from session 1.
See Model Context Protocol (MCP) training tracksShip your first Model Context Protocol (MCP) system
Architecture design, production implementation on Claude API and MCP, full observability, and a real handoff. Working agents, not slides.
Explore Model Context Protocol (MCP) consulting