Data & AI platform by Data Analytics
Commissioning an MCP server: from a bounded need to a managed connection
You want to make business data or specific functions available to a suitable AI client. An MCP server can provide an interface, but development starts with your process, sources and access rules. ENABLE is the manageable Data & AI platform from Data Analytics for connecting and structuring data, analysis, apps and AI. Together, you can explore which server setup fits. Scope, feasibility and results depend on the chosen implementation. An engagement therefore requires concrete agreements, not the assumption that every source connects immediately or that absolute security follows.
The examples on this page are hypothetical situations, not customer cases.
Meet our team
Trusted by data-driven organisations
01
Make the engagement and exclusions concrete
Describe the target users, initial use case and questions the server must support. Also record what is outside the engagement: for example writing, additional business entities or historical data migration. Inventory source APIs, documentation, access rights, network requirements and existing definitions. Without that information, meaningful technical scoping or scheduling is not possible.
Agree which parties provide information and test access, and who makes decisions. Distinguish the data foundation, MCP interface and client configuration as separate components. Building a server does not repair incorrect accounting entries or missing customer numbers. Where necessary, data integration or data model improvements precede a useful AI application.
02
Design tools for the chosen client and permissions
Check whether your intended client and subscription support the required features, transport and authentication method. Then specify tool names, input schemas, result structures, error messages and limits. Prefer purpose-built functions with limited parameters. A general execution channel for arbitrary commands requires different risk assessments from a tool for a bounded revenue selection.
Determine where identity and authorisation are checked and how accounts are revoked. Where appropriate, start with read-only access, technically enforced on the server and data source. Writing requires separate tools and scopes, explicit human approval and server policies. Include idempotency, rollback or compensation when actions have consequences that cannot automatically be undone.
03
Decide on processing and the management environment in advance
Explicitly choose whether selected data may be processed by an external LLM. Discuss contracts, location, retention periods and relevant service settings before supplying test data. An MCP server in your own environment does not mean tool results stay there. For a private model route, likewise assess the actual data flows, administrator access and responsibility for updates.
Agree hosting, secrets management, monitoring and incident handling. Keep audit logging minimal and protected, with clear access and retention periods. Treat tool results as untrusted input and test prompt injection. Document dependencies on source vendors and client features; changes outside the development team can later affect availability, permissions or how tools are used.
04
Make acceptance demonstrable through concrete tests
Before development, specify when you will accept delivery. Use verifiable source results, agreed definitions and representative roles. Test parameters, result limits, missing data, timeouts and denied access. Also check that users cannot reach restricted data through another tool. An attractive demonstration is no substitute for these acceptance criteria.
Hypothetical example: a sales manager may request quarterly revenue for their own region. The test compares totals with a documented source selection, denies another region and shows a clear message if the source fails. Specify who reviews the test and which findings must be resolved first. Outcomes remain dependent on available source quality and supported client capabilities.
05
Arrange ownership after initial delivery
Agree ownership and usage rights for custom work, source code, configuration and documentation. Assign owners for data, server, client and user management. Specify who performs updates and who approves changes. Discuss handover, access to management information and ending the engagement, so the capability does not remain dependent on undocumented knowledge.
Source schemas and APIs change. Therefore agree schema updates, version control, regression tests and compatibility checks, including a procedure for reverting or temporarily disabling the service. Data Analytics can develop a scoped proposal for alignment with ENABLE with you. Base follow-up decisions on agreed tests and management workload, not on a general promise of speed, savings or flawless operation.
Frequently asked questions
What should you provide for an initial discussion?
Bring the use case, intended client, source descriptions and access requirements. Identify the process and data owners. This enables a focused definition of the necessary discovery work.
Can every database be connected immediately?
That is not guaranteed. Reachability, available interfaces, permissions, data quality and client support determine the options. Integration, additional modelling or changes by the source vendor may be needed first.
Who maintains the server after delivery?
You define that contractually. Explicitly allocate responsibility for hosting, updates, schema changes, access management and incidents. Also agree who performs regression tests when a source or client changes.
Is a working server enough for reliable AI?
No. Source quality, tool definitions, client setup, model selection and human review also matter. Accept the entire agreed usage route through concrete tests; technical connectivity alone does not establish substantive reliability.
Technical documentation
Discuss your question with Data Analytics
Which sources, users and checks does your application need? Discuss your question in an ENABLE demo.
Meet our team