Model Context Protocol (MCP) is the open standard that lets AI agents access your systems —ERP, CRM, databases, internal APIs— without rewriting a separate integration for every model. Summum IA implements, secures and operates your MCP servers so your company's AI acts on real data with controlled permissions.
Model Context Protocol was published by Anthropic in November 2024 and donated to the Agentic AI Foundation (AAIF) under the Linux Foundation in December 2025. By March 2026 the ecosystem had surpassed 10,000 active MCP servers and 97 million monthly SDK downloads, with native support from OpenAI, Google, Microsoft, AWS and the leading agent frameworks. This growth makes MCP the de facto standard for connecting language models to enterprise tools, much as REST unified web APIs two decades ago.
For an SME, the problem MCP solves is concrete: every AI agent —a sales copilot, a support assistant, a billing automation— previously needed its own integration code for each tool. With MCP, the integration is written once as an MCP server and any compatible model consumes it in a standardised way. The result is less maintenance, centralised auditing of what the AI does with which data, and the ability to swap models without rebuilding integrations.
Summum IA designs the MCP architecture tailored to your stack: it identifies which tools should be exposed as MCP resources or tools, defines permission schemas by role (who can read, who can write, what data is out of scope), implements the servers on your infrastructure or in the cloud, and establishes the monitoring and audit logs that responsible operations require. Technical governance of the models consuming those tools falls under our LLMOps service; integrating ERP or eCommerce systems with business APIs is handled by the Summum Sistemas team.
We inventory the systems that need to be accessible to AI agents (ERP, CRM, databases, SharePoint, internal REST APIs) and classify each one by data sensitivity and whether write or read-only access is required. We produce the dependency map before writing a single line of code.
We define how many MCP servers are needed, their topology (local, remote, multi-tenant), the authentication model (OAuth 2.0, API keys, service tokens) and the role-based permission policy. We deliver the architecture document for review before development begins.
We implement MCP servers in Python or TypeScript using the official SDK. Each server exposes the agreed resources and tools, includes input validation, error handling and the required security controls. We deploy on your infrastructure or in a cloud environment with high availability.
We run end-to-end integration tests with the real MCP clients (agents, copilots, n8n flows). We configure audit logs, anomalous-usage alerts and the operations dashboard. We train the internal technical team and deliver the operations documentation.
The operational detail: what we deliver as part of the work and what we keep alive afterwards.
Integration surface audit
Inventory of the company's tools, APIs and databases, classified by sensitivity and AI access requirements.
MCP architecture document
Server topology, authentication model, role-based permission policy and operational cost estimate.
MCP server development
Implementation of MCP servers with the official SDK, unit tests and schema-contract validation.
Deployment and hardening
OAuth / API key authentication configuration, in-transit encryption, environment isolation and secrets management.
Audit logs and alerts
Structured logging of every tool call, with anomalous-usage alerts and configurable retention for compliance.
Training and operational documentation
Handover session for the internal technical team, operations runbook and guide for onboarding new connectors.
MCP integration is the connection layer between your systems and AI agents: it complements LLMOps technical governance, n8n automation flows, and the ERP system integration managed by Summum Sistemas.
Once systems are connected via MCP, LLMOps ensures that the models consuming them perform with the quality and security expected in production.
View service → ian8n acts as a flow orchestrator that can invoke MCP tools to build complex automations with real context from the company's systems.
View service → sistemasSummum Sistemas exposes the ERP or eCommerce business APIs that are then turned into MCP servers for AI agents.
View service →A conventional REST integration is designed for one application to call another in a deterministic way. MCP is designed for an AI agent to decide in real time which tool to invoke and with which parameters, following a standardised protocol. This allows the same MCP server to be reused with any compatible model, new tools to be added without touching the client, and all AI actions on company systems to be audited centrally.
MCP is a transport protocol and does not include security on its own: security is defined by the implementation. At Summum IA we apply OAuth 2.0 authentication or short-lived service tokens, TLS encryption for all traffic, granular role-based permissions (a support agent cannot write to the financial ERP), and audit logs for every tool call. For particularly sensitive data, the MCP server can be deployed on the company's private infrastructure so data never leaves the perimeter.
Any system that has an API or a connector: ERP (Odoo, Sage, Dynamics), CRM (Salesforce, HubSpot), SQL or NoSQL databases, SharePoint and Google Drive, project management tools (Notion, Jira, Linear), email and calendar services, and custom-built internal REST APIs. If a system has no API, we assess whether it is feasible to develop a connector on top of its database or export.
A simple MCP server with two or three tools on an existing REST API can be in production in two to three weeks. A project with four to six connectors, a role-based permission model and clearly separated staging and production environments typically takes six to ten weeks. Most of that time is not code: it is data analysis, permission definition and integration testing with the real agents.
Not necessarily. If your current agent supports MCP clients (Claude, GPT-4o, Gemini, LangChain and many others already do natively in 2026), it is enough to point the client at the MCP server we develop. If you use a model or framework that does not yet support it, we can design an adaptation layer or evaluate migration. In any case, the MCP server we build is reusable with any compatible client, present or future.