correlation

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

With no annotations, the description must fully disclose behavior. It fails to explain output format (single number), edge cases (e.g., different lengths, missing values), or assumptions (linearity). The method options are listed but not elaborated.

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: two sentences for the purpose plus a brief parameter list. No redundancy, and the key action (compute correlation) is front-loaded.

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?

For a simple 3-parameter tool with no output schema, the description covers inputs and purpose but omits output details and constraints (e.g., data must be numeric, equal length). It is adequate but not fully comprehensive.

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 has no descriptions (0% coverage). The description adds essential meaning: x and y are samples, must have same length, and method takes 'pearson' or 'spearman'. This compensates for the schema gap, though it could detail method behavior.

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 'Compute the correlation coefficient between two samples', specifying the verb (compute) and resource (correlation coefficient) with explicit method options. It distinguishes itself from siblings like 'dataframe_correlation_matrix' which operates on dataframes.

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?

No guidance is provided on when to use this tool versus alternatives. It does not mention conditions, prerequisites, or exclusions. The description only explains parameters, leaving the agent to infer usage context.

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