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Service 06

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.

Talk to an engineer What you get

In one paragraph

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.

Deliverables

What you get

  1. 01 MCP servers for your internal systems, such as ERP, CRM, ticketing, data warehouse and document stores
  2. 02 OAuth-based authentication, so agents act with the permissions of the user or a scoped service identity
  3. 03 A gateway and a registry that control which agents can use which tools
  4. 04 Search over your documents that respects the existing access rules
  5. 05 Agent2Agent (A2A) connections between agents from different vendors
  6. 06 An audit log for every tool call, with the user, the agent and the arguments

Where it fits

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.

How we work

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.

FAQ

Questions about mcp and integration

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

Often combined with

All services

  • Service 01

    AI agents in production

    We design, build and run AI agents that work inside your systems. They read, decide, call tools and hand the case to a person when a rule says so.

  • Service 07

    Evals, security and AI governance

    We measure whether your AI systems work, test how they fail and document them for auditors, with evals, red-team tests, tracing and compliance mapping.

Tell us which process you want to hand to an agent

A 30-minute call with an engineer. We will tell you whether an AI system is the right tool for it, and what it would take to run it in production.

Talk to an engineer contact@ninalabs.ai