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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsTo test whether a model picks the right MCP tool when several tools have similar purposes, give it a fixed set of tasks and a recorded tool catalog, then score tool selection, argument validity, and execution success separately. The method below is a proposed evaluation design based on official MCP client interfaces—not a standardized or empirically validated MCP benchmark.
What the test measures
MCP tools are executable functions that let a model take actions or retrieve information. The MCP specification describes them as model-controlled, so a useful evaluation asks whether the model chooses the intended function for a particular request—not merely whether a call eventually returns a result. MCP tools specification
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An MCP client can list the tool catalog before calls are made. The official Python SDK documents tool definitions with a name, optional title, description, and input schema; it describes these definitions as what a host would give a model. The official C# SDK documents JSON Schema 2020-12 parameters and notes that parameter descriptions help LLMs understand expected inputs. Python SDK · C# SDK tool documentation
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Write task cases and an answer key
For each user request, record the intended tool and, where it matters, the expected arguments. Include at least one case for every tool in the catalog, especially tools with overlapping purposes. Keep the cases realistic and distinguishable enough that a reviewer can explain why a particular tool is correct. This coverage plan is a proposed testing practice, not an MCP specification requirement.
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Capture the catalog and run configuration
Record the exact tool name, title if present, description, and input schema exposed to the model. Also record the model and version, relevant settings, and the prompt for each run. The Python SDK’s
list_tools()provides a way to inspect the definitions presented through the client interface. -
Change one definition field at a time
Start with a baseline catalog. For a comparison, change only one field—name, description, or input schema—while holding the task cases and other settings constant. This makes it easier to attribute a change in behavior to the field you altered rather than to several simultaneous edits.
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Repeat the same cases
Run the same task cases against each model or catalog configuration. Report the number of runs and the conditions used. A few selected prompts can reveal examples, but they do not establish a model’s general ability to distinguish tools.
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Score the stages separately
For each attempt, record whether the model selected the intended tool, whether its arguments match the task and the tool’s schema, and whether the call completed successfully. The Python client exposes tool calling and an
is_errorresult field; tool errors can be returned to the model. A runtime error by itself does not prove that the model chose the wrong tool. -
Classify failures before revising the catalog
Separate wrong-tool selections from correct-tool calls with invalid arguments, and from execution failures after a valid call. Review representative examples before changing a tool description or schema; otherwise, you may fix the wrong part of the workflow.
Compare models or catalog versions on the same axes
Keep task cases and execution conditions constant, then compare the following outcomes. These are practical evaluation axes inferred from the documented interface; MCP does not prescribe this scorecard.
| Measure | What to record |
|---|---|
| Correct-tool selection | Whether the chosen tool matches the answer key for the request. |
| Argument validity | Whether the arguments fit the intended task and the selected tool’s input schema. |
| Call success | Whether the tool call completed successfully, recorded separately from selection and argument quality. |
| Repeatability | Whether outcomes remain consistent across repeated runs under the recorded conditions. |
| Sensitivity to definition changes | Whether changing the name, description, or schema alone changes selection or argument outcomes. |
Do not treat tool annotations as guarantees
MCP annotations such as readOnlyHint, destructiveHint, idempotentHint, and openWorldHint are hints, not guarantees. The MCP blog says clients should treat them as untrusted unless they come from a trusted server. If annotations are part of your evaluation, test the model’s response to them separately from tool-choice correctness; an annotation does not establish what a tool actually does. MCP blog: Using server annotations
What results can—and cannot—show
This procedure can show how a particular model and configuration behaved on your recorded tasks and catalog. It cannot, by itself, establish a universal capability score. The official sources cited here document tool definitions and client behavior, but do not establish a canonical benchmark, a model ranking, or a reliable expected accuracy for distinguishing similar MCP tools. Report your cases, sample size, model, settings, and catalog alongside any results, and avoid presenting the procedure as an official MCP standard.
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