Your internal tools, agent-ready:
one protocol, not ten custom scripts
A business building more than one AI agent usually ends up writing a custom integration script for every tool each agent needs to touch, which means the same database connection or API client gets rebuilt slightly differently every time. We wrap your internal tools and data sources in MCP servers once, so any agent you build afterward can use them through one standard protocol instead of a new script each time.
The process today
A business that builds its first AI agent usually wires it directly to the one or two tools it needs with a one-off script, which works fine until a second agent needs some of the same data or the same system, and ends up with its own slightly different integration built from scratch. Over a few agents, this turns into a pile of near-duplicate scripts, each with its own quirks, its own access logic, and its own place to go wrong.
The second cost is that access control ends up inconsistent. One script might expose more of a database than the agent actually needs because scoping it down felt like extra work at the time, and nobody audits that gap until something unexpected happens.
The third is that every new agent project starts from zero on the integration side, re-solving the same connection problem instead of building on what the last agent project already figured out, which slows down exactly the kind of experimentation a business wants to be able to do quickly.
What the agent does
We build an MCP server for each internal tool, database or API your agents need to reach, following the Model Context Protocol standard rather than a one-off script. Each server exposes only the specific actions you scope it to, read access to a dataset, a specific write action behind an approval gate, nothing broader by default, and documents exactly what it offers so any agent, current or future, connects to it the same predictable way.
Once the servers exist, building the next agent that needs the same data or system is largely a matter of pointing it at the existing MCP server rather than writing new integration code, which is the actual payoff: the second and third agent 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 stays with humans
Deciding what each MCP server is allowed to expose, and which actions require an approval step rather than running automatically, is a decision your team makes up front and can revise. Any write action with real consequences, deleting data, changing a record with financial or legal weight, stays gated behind explicit approval regardless of how the agent calling it is built. Reviewing which agents have access to which servers over time is an ongoing governance task we hand off to your team with clear documentation.
Guards
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, and 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 since a well-scoped set of MCP servers makes orchestrating several agents far simpler, and 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 just 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, depending 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. Building to it once, instead of a custom integration per agent, means every future agent you build can reuse 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.