MCP servers and integrations
The Model Context Protocol (MCP) gives AI models governed access to your databases and internal tools through one standard — instead of a fragile one-off integration per tool. We design and build MCP servers with the permissions and audit trails your infrastructure team can sign off on.
Every AI project eventually hits the same wall: the model needs access to real systems — a data warehouse, a CRM, a ticketing tool — and nobody wants to open a hole in the perimeter for it. MCP solves this with a standard connector layer: tools, resources and prompts exposed in a uniform way, with the same access control as any internal service.
We have built MCP servers on Dremio and Postgres, with governed query execution, row limits and timeouts, access through the user's own credentials, and full logging of every query together with the question that produced it. The result: business users ask questions in natural language, and the data team keeps the same control as before.
What is included
Dremio, Postgres, REST
Servers for data platforms, databases and REST APIs — schema discovery, governed queries, result summaries.
Your permissions, unchanged
Access runs through the user's own credentials, so existing permissions and row-level security apply unchanged.
Audit trail built in
Every tool call is logged with its context: who asked what, which query ran, what came back.
Works with your clients
Claude, ChatGPT, IDE agents and your own agent frameworks — one server serves them all.
Hardened by default
Row limits, timeouts, read-only modes and rate limits instead of an open database connection.
How we work
- 1
Map the tools
Which systems, which operations, which permissions. We define the tool surface with your infrastructure team.
- 2
Build the server
MCP tools with governed access, logging and hardening — tested against your real schemas.
- 3
Roll out to agents
Connect your AI clients, verify permissions in practice, and document operations for your team.
Frequently asked questions
What is MCP (Model Context Protocol)?
MCP is an open standard that lets AI models call external tools and data sources through a uniform connector layer — tools, resources and prompts instead of one-off integrations. It is supported by Claude, ChatGPT and most agent frameworks.
Is it safe to give an AI model database access?
It is, when access is governed: the user's own credentials, read-only modes where appropriate, row limits and timeouts, and an audit log for every query. That is exactly how Dataotter builds MCP servers.
Which systems can you connect?
Data platforms like Dremio, databases like Postgres, and REST APIs. If your system is reachable from your network, we can expose it as governed MCP tools.