Give agents a clear interface to your tools.
Model Context Protocol, or MCP, provides a shared interface between compatible AI clients and external capabilities. 0OP develops focused MCP integrations around the information and actions an agent actually needs.
Explore the scope ↓0op.dev/mcpWhere the work gets stuck
An agent may understand a request but have no reliable way to read the relevant records or perform the next action. Exposing an entire backend creates a different problem: too many capabilities with unclear consequences. A useful tool interface narrows that gap. For example, finding an order and preparing a status summary can be separate from changing the order itself.
A concrete example
Example scope: let a compatible AI client retrieve order status from an approved business system. Reading an order is separated from changing or cancelling it.
Input
The chosen AI client, supported backend interfaces, order identifiers, and an agreed set of read-only capabilities.
Output
A focused agent-facing connection with traceable lookups, understandable errors, and documented access boundaries.
- Define narrowly described tools with validated inputs and explicit result formats.
- Connect authorized backend operations and return clear missing-record or access errors.
- Test realistic client requests and confirm that unsupported changes cannot be performed.
When it fits
Useful when compatible AI clients need reusable tool access. A fixed application integration may be simpler through a direct API.
What you could receive
- MCP server and tool contracts
- Backend adapter and permission checks
- Client testing and access revocation notes
What we need to start
- Chosen client and connection requirements
- Backend access and example responses
- A capability list and authorization policy
Capture a starting scope with the workflow templates. For the design choice, read agents vs automation.
Workflows built around MCP
Focused MCP servers
Expose a small set of well-described capabilities over approved systems. Use clear inputs and results so the client can tell what a tool does and what a successful response means.
Business system adapters
Translate agent requests into supported operations on existing services. Validate inputs, preserve record identifiers, and return errors that explain whether the task can be retried or needs a person.
Permission and action boundaries
Separate information access from changes. Scope credentials to the intended use and design confirmation requirements around the actions the connected client is authorized to perform.
How the project comes together
Choose the client and task
Confirm the intended client’s supported connection method and available access. Start from a concrete workflow rather than exposing every operation a service offers.
Design the tool contract
Define names, descriptions, inputs, outputs, and failure behavior. Test realistic requests and ambiguous inputs against the underlying system.
Verify the complete connection
Check authentication, permission boundaries, and tool behavior in the actual client. Record actions and document how to revoke access or stop the integration.
Keep the boundaries explicit
MCP is an interface, not an authorization policy or a guarantee that an agent will choose correctly. Client behavior and supported features vary. Untrusted content, sensitive information, and state-changing actions need explicit handling beyond simply establishing a connection.
Further reading
The official Model Context Protocol introduction explains how AI applications connect to external tools and data.
Questions about MCP
Does MCP replace my existing API?
Usually not. An MCP server can provide an agent-facing interface over an existing API. The underlying system remains responsible for its business rules and permitted operations.
Do all AI applications support the same MCP features?
Do not assume they do. We verify the chosen client, connection method, authentication needs, and required capabilities before committing to the integration design.
Should every integration use MCP?
No. A direct API connection may be simpler for a fixed application workflow. MCP is useful when compatible AI clients need a clear, reusable interface to selected capabilities.
What should stop being manual?
Bring a recurring task, an example input, and the outcome you need. We can use that to define a practical first scope.
Discuss it with Oğuz ↗0OP · Zero Operation Development
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