# MCP and systems integration

> We connect agents to your systems through Model Context Protocol (MCP) servers and APIs, with real authentication, scoped permissions and audit logs.

- Page: https://www.ninalabs.ai/services/integration/
- Publisher: Nina Labs AI LLC
- Updated: 2026-10-07

## Definition

The Model Context Protocol (MCP) is an open standard that lets AI applications use external tools and data through one interface. An MCP server exposes a system, such as a CRM or a data warehouse, to any compatible agent.

## What you get

- MCP servers for your internal systems, such as ERP, CRM, ticketing, data warehouse and document stores
- OAuth-based authentication, so agents act with the permissions of the user or a scoped service identity
- A gateway and a registry that control which agents can use which tools
- Search over your documents that respects the existing access rules
- Agent2Agent (A2A) connections between agents from different vendors
- An audit log for every tool call, with the user, the agent and the arguments

## Typical use cases

- **Systems of record:** Read and write access to ERP and CRM data through a small set of well-designed tools.
- **Company knowledge:** Search across wikis, drives and tickets that returns only what the user is allowed to see.
- **Data and analytics:** Governed access to the warehouse, with query limits and approved metric definitions.
- **Agents from several vendors:** An agent in one platform hands work to an agent in another through A2A, with a shared audit trail.

## Context decides the result

When an agent fails in production, the cause is usually missing context or a poorly designed tool, not the model. A good integration layer gives the agent the right data, in the right shape, with the right permissions.

## Tools designed for agents

An API built for programs is often a bad tool for an agent. We design tools for how models work:

- Fewer, higher-level tools with clear names and descriptions.
- Structured errors that tell the agent what to do next.
- Pagination and filters, so results fit in the context window.
- Idempotent writes, so a retry does not create a duplicate.

## Identity and permissions

Each agent has its own identity. When it acts for a user, it uses that user's permissions and nothing more. Shared admin keys are not allowed. Every call is logged with who asked, which agent acted and what changed.

## Open standards

We build on open protocols, so your integrations outlive any one vendor. MCP connects agents to tools and data. A2A connects agents to other agents.

## Stack we use

Model Context Protocol (MCP), Agent2Agent (A2A), OAuth 2.1, Microsoft Entra Agent ID, Okta, Amazon Bedrock AgentCore Gateway, PostgreSQL and pgvector, OpenSearch

## What we measure

- Tool call success rate and latency
- Share of agent failures caused by missing or wrong context
- Permission violations caught by the gateway
- Time to connect a new system

## Frequently asked questions

### Why build MCP servers instead of direct API calls?

An MCP server works with any compatible agent, whether it is from Anthropic, OpenAI, Google or Microsoft or built in-house. You build the integration once and keep it when you change models or platforms.

### Can an agent see data the user cannot?

Not in what we build. The agent acts with the user's identity or with a service identity that has a narrow, written scope. The gateway checks every call.

### Do you only build new MCP servers?

No. We also review and harden existing servers and third-party connectors for authentication, input validation and over-broad permissions.

## Related services

- [AI agents in production](https://www.ninalabs.ai/services/agents/)
- [Evals, security and AI governance](https://www.ninalabs.ai/services/evals-governance/)

## Contact

Talk to an engineer through the [contact form](https://www.ninalabs.ai/contact/) or by email at contact@ninalabs.ai.