fishers_exact_test

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 Fisher's exact test but does not disclose behavioral traits such as output format, error handling, computational limits, or assumptions (e.g., small sample sizes). This is inadequate for a statistical inference tool with zero annotation coverage.

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 concise and front-loaded, stating the core purpose in a single sentence. The additional context (Domain: statistics, Category: inference) is brief and relevant. There is no wasted text, 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 low schema coverage, the description is incomplete. It lacks details on parameters, output values (e.g., p-value, odds ratio), and usage context. This is insufficient for effective tool invocation by an AI agent.

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?

The input schema has 5 parameters (a, b, c, d, alpha) with 0% description coverage in the schema. The description does not add any meaning beyond the schema—it does not explain what a, b, c, d represent (e.g., counts in a 2x2 table) or the role of alpha (significance level). This fails to compensate for 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 Fisher's exact test for 2x2 contingency tables to test association between two categorical variables.' It specifies the statistical test, the table format, and the goal (testing association). However, it does not explicitly differentiate from sibling tools like 'chi_square_test' or 'proportion_test', which might also handle categorical data analysis, so it misses full sibling distinction.

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 provides no guidance on when to use this tool versus alternatives. It mentions the domain (statistics) and category (inference), but does not specify scenarios, prerequisites, or comparisons to siblings like 'chi_square_test' or 'proportion_test'. This leaves the agent without clear usage context.

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