MCP Math Server

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It states the tool performs a statistical test but doesn't disclose behavioral traits such as what the output includes (e.g., t-statistic, p-value, confidence interval), assumptions (e.g., normality of differences), or error handling (e.g., for unequal array lengths). The description is minimal and lacks operational details needed for effective use.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise—a single sentence with no wasted words. It front-loads the core action ('Perform paired t-test') and includes essential qualifiers ('dependent samples,' 'test whether paired observations differ significantly') and domain context. Every part earns its place, making it efficient for quick understanding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a statistical test, no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It lacks details on parameters, output format, assumptions, and usage compared to siblings. While concise, it doesn't provide enough context for reliable tool invocation, especially for an AI agent needing to interpret results or handle errors.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It doesn't mention any parameters, leaving 'before', 'after', and 'alpha' undocumented. The description hints at 'paired observations' which relates to 'before' and 'after' arrays, but doesn't explain their required format (e.g., equal length, numeric) or 'alpha' as the significance level. This is inadequate given the low schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Perform paired t-test (dependent samples) to test whether paired observations differ significantly.' It specifies the statistical test (paired t-test), the sample type (dependent/paired), and the goal (test for significant differences). However, it doesn't explicitly differentiate from sibling tools like 't_test_one_sample' or 't_test_two_sample' beyond mentioning 'paired' and 'dependent samples'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context through 'paired observations' and 'dependent samples,' suggesting this tool is for before-after or matched-pairs data. However, it doesn't provide explicit guidance on when to use this versus alternatives like 't_test_two_sample' (for independent samples) or 'wilcoxon_signed_rank' (non-parametric alternative). No exclusions or prerequisites are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.