Node AI.07

MCP and tool integrations

Connect AI models to your systems through interfaces you control.

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Model Context Protocol (MCP) servers and custom tool layers that connect AI models and agents to your internal tools, databases and APIs, safely.

The problem

An AI model is only as useful as what it can reach. Teams want assistants that can look up an order, query a database or open a ticket, but giving a model raw credentials is not an option.

Model Context Protocol (MCP) gives AI clients a standard way to discover and call tools. The work is in designing those tools: narrow enough to be safe, clear enough for a model to use correctly, and logged.

What we build

MCP servers that expose your internal systems, databases and APIs to AI clients and agents
Tool design: typed inputs and outputs, descriptions written for models, read and write separation
Authentication and per-user permissions, so the AI can only do what the user could
Rate limits, input validation and audit logging on every call
Connectors for common business systems and custom in-house tools

How it works

AI clients connect to an MCP server, which exposes a curated set of tools. Each call is authenticated, validated, executed against the underlying system and logged.

Step 1InventoryList the systems and actions the AI should reach, and who is allowed to use each.
Step 2Design toolsTurn each action into a small, typed tool with a description the model can follow.
Step 3SecureAdd authentication, permission checks, validation and rate limits.
Step 4Test with modelsRun real tasks through several models to confirm they call the tools correctly.
Step 5Deploy and observeDeploy the server and watch call logs for errors and misuse.

Deliverables

MCP server or tool layer, deployed
Tool catalogue with permissions and examples
Security review notes and audit logging
Client configuration for your AI tools and agents

Typical stack

Python
LLM and agent frameworks
Google Cloud

We choose tools to fit your environment; this is where we usually start.

Ways to work with us

Build and HandoverA defined system, delivered, documented and handed to your team.Engagement Forward Deployed EngineersOur engineers inside your team until it runs in production.Engagement

Questions

What is MCP?

Model Context Protocol is an open standard for connecting AI applications to tools and data. An MCP server describes the tools it offers, and compatible AI clients can discover and call them.

Do we have to use MCP?

No. If your agents run in a framework that uses its own tool format, we build the same tool layer for that framework. MCP is useful when several AI clients need the same tools.

How do you stop an AI from deleting data?

Write and delete actions are separate tools with their own permissions, can require approval, and are scoped to the calling user. Many deployments start read-only.

Adjacent nodes

Part of AI & Agents.

Agentic AI systemsAgents that plan, use your tools, and wait for a yes.AI.01 APIs, integrations and workflow automationYour systems talking to each other, so people stop copying between them.PROD.03 AI governance and responsible AIClear rules for how your organisation uses AI, that people actually follow.ADV.02

Talk to us about mCP and tool integrations for your business.

Send a short brief. We reply by email, usually with questions before we propose anything.