Your internal tools, agent-ready:
one protocol, not ten custom scripts
Build more than one AI agent and you end up writing a custom integration script for every tool each one touches. The same database connection or API client gets rebuilt slightly differently each time. We wrap your internal tools and data sources in MCP servers once. Every agent you build afterward reaches them through one standard protocol instead of a new script.
Why the same script keeps getting rebuilt
The first AI agent a business builds usually gets wired straight to the one or two tools it needs, with a one-off script. That works fine until a second agent needs the same data or system and ends up with its own, slightly different integration built from scratch. A few agents later, that is a pile of near-duplicate scripts, each with its own quirks and its own place to go wrong.
Access control drifts along the same path. A script might expose more of a database than the agent actually needs, because scoping it down felt like extra work at the time. Nobody audits that gap until something unexpected happens.
And every new agent project starts the integration side from zero, re-solving a connection problem the last project already solved. That slows down exactly the kind of experimentation a business wants to move fast on.
What we build
We build an MCP server for each internal tool, database or API your agents need to reach. It follows the Model Context Protocol standard instead of a one-off script. Each server exposes only the specific actions you scope it to: read access to a dataset, maybe one write action behind an approval gate, nothing broader by default. It documents exactly what it offers, so any agent, current or future, connects to it the same predictable way.
Once the servers exist, the next agent that needs the same data mostly points at the existing MCP server instead of getting new integration code. That is the actual payoff. Agent two and three get noticeably faster and cheaper to build.
Typical scope: your internal databases, CRM or ERP, file storage, and any internal API your team already has, each wrapped as its own scoped MCP server.
What your team still decides
Your team decides up front what each MCP server is allowed to expose, and which actions need an approval step instead of running automatically. That decision can be revised any time. Any write action with real consequences, deleting data or changing a record with financial or legal weight, stays gated behind explicit approval. That holds no matter how the calling agent is built. Reviewing which agents have access to which servers over time is an ongoing governance task. We hand it to your team with clear documentation to run it from.
Guardrails
Every tool call through every MCP server is logged, so you can see which agent requested what, when, and what it got back. Read and write actions are separated by design, and write actions beyond a trivial, reversible scope are gated behind approval. Each server is tested against real queries and actions before any agent is allowed to use it live. Access can be revoked per server or per agent instantly if something looks wrong.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Single automation | from $1,200 | MCP servers for 2 to 4 tools, access scoping, documentation | 2 to 3 weeks |
| Department package | from $4,000 | MCP coverage across your core internal systems, approval gating, reusable for all future agents | 5 to 9 weeks |
Running cost is usually $30 to $100 a month in hosting and model usage depending on how many agents and how much traffic uses the servers.
Related
This pairs well with multi-agent orchestration for operations. A well-scoped set of MCP servers makes orchestrating several agents far simpler. It also pairs with agent approval queue and audit log for the governance layer on top. See the AI agents service page and the automation-everything overview for full package details. For real builds on internal tooling and multi-brand data systems, see the two-brand analytics hub case study and the factory ERP recovery case study.
Writing a new integration script for every agent you build? Get in touch and we will map which of your internal tools are worth wrapping once.
Tired of doing this by hand? We can take the whole routine off your team, not only this step: Routine takeover, from $400 →
FAQ
How much does it cost to set up MCP integrations?
From $1,200 for MCP servers covering 2 to 4 internal tools or data sources, live in 2 to 3 weeks. Larger internal tool estates usually run $2,500 to $5,000.
How long before it is live?
2 to 3 weeks. It depends on how many tools, databases or APIs need an MCP server and how much access scoping each one needs.
What is MCP and why does it matter?
Model Context Protocol is an open standard for connecting AI agents to tools and data. Build to it once instead of a custom integration per agent, and every future agent you build reuses the same connections without rework.
Can this expose something an agent should not be able to do?
Only what you scope it to. We separate read and write actions, gate anything risky behind an approval step, and an agent only sees the tool calls it has been explicitly granted. Nothing more.
Is our internal data secure in this setup?
Data stays inside your own infrastructure. The MCP servers run where you choose to host them, and every tool call is logged, so you can see exactly what any agent requested and when.