When an AI agent works with your product, it needs a way to find information and take action. The Model Context Protocol (MCP) gives AI applications a shared way to access those capabilities. Through an MCP server, you can provide tools for checking deployments or updating tickets, along with context such as documentation. The same server can work across compatible applications, reducing the need for separate integrations.
The ecosystem around that connection has grown quickly. In December 2025, Anthropic reported more than 10,000 active public MCP servers. By July 2026, MCP’s maintainers reported nearly 500 million monthly downloads across its Tier 1 software development kits. For developer products, that growing ecosystem creates more opportunities to reach users through the AI applications they already use. Agent evaluations build on that by showing how coding agents use MCP to choose tools, interpret responses, and complete developer tasks.
Building and connecting your MCP server
Developers have traditionally worked with products through interfaces, CLIs, and APIs, using documentation to find their way. As coding agents take on more of that work, MCP gives them a shared way to discover and use your product’s capabilities.
Building an MCP server starts with choosing which operations agents can use and exposing them as tools. Each tool has a name, a description, and a definition of the inputs it accepts, and connects to your existing APIs or application code. The official MCP SDKs handle communication between your server and the AI application.
Building and connecting your MCP server
Once connected, an agent can call a tool with the required inputs. Your server runs the corresponding code and returns the result, such as deployment logs or a ticket’s current status. Setup may also include signing in or supplying credentials so the tools operate with the connected user’s account permissions.
Test your MCP server with MCP Inspector
Once the server is connected, you can check its behavior directly with MCP Inspector. It shows which tools are available and lets you follow a call from its inputs to the returned result. With known starting data, you can see whether the operation reaches the correct resource and returns what you expect.
You can then explore how the server responds when a request is incomplete or the connected account lacks access. Your team writes the product-specific error messages that explain what happened and what would help the task continue. Running these workflows as agent evaluations lets you see whether agents can access your tools, understand the feedback, and take a useful next step inside the applications your users work with. For tools that change data, the evaluations can also check that the correct resource was created, updated, or deleted and that unrelated data stayed unchanged.
Evaluate MCP tool use with coding agents
In our previous post, we covered how to build evaluation tasks that test how well agents handle the work developers do with your product. The same approach applies to MCP evaluations, with a closer look at which tools agents use and how they use them.
Before interpreting a missing tool call, check whether the tool was available to the agent. If it wasn’t, review setup and discovery. If it was, consider whether its name, description, and accepted inputs made it a clear choice for the task.
The table below shows which trace evidence can help explain how agents use your MCP tools. Some applications don’t show discovery steps or identify which server handled each call. Client and server logs can help fill those gaps, while separate outcome checks verify whether the task produced the intended result.
What to look for in an MCP agent trace
| MCP behavior | Evidence in the trace |
|---|---|
| MCP tool use | Whether the agent called relevant tools on your MCP server. |
| Correct inputs | Whether calls used valid arguments and targeted the intended resources. |
| Use of results | Whether the agent used your server’s responses to guide its actions and support its answer. |
| Error recovery | Whether the agent adjusted after errors and checked uncertain writes before retrying. |
Some tasks can explicitly ask the agent to use your MCP server, with successful use included in the evaluation criteria while allowing different valid tool sequences. Other tasks can leave the approach open so you can see whether agents choose your MCP tools. Recording the route separately from the outcome helps distinguish task success from successful use of your server. Repeating each task from the same starting conditions helps you assess consistency across runs.
Keep these tasks as a repeatable evaluation set and rerun them after changing your tools’ names, descriptions, inputs, or responses. When comparing server versions, restore the starting data and keep the client, model, permissions, and other available tools consistent. Compare the trace evidence above alongside verified outcomes across repeated trials to see where behavior improved or regressed.
Conclusion
Testing your MCP server with coding agents shows how the integration supports real developer workflows. Following the agent’s choices reveals how it selects tools, supplies inputs, and responds to errors. That evidence helps you identify where clearer tool descriptions, more useful results, or better error messages could make the experience more dependable.
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