correlate

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

With no annotations, the description discloses the join on shared keys, return of correlation and sample size, and schema validation. However, it does not detail side effects, permission requirements, or error handling, which is slightly incomplete.

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 (4 sentences), front-loads the core purpose, and includes an illustrative example. Every sentence adds value without redundancy.

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 tool's complexity (cross-table join correlation), the description covers essential aspects: statistical method, join keys, return values, and validation. It lacks details on handling missing data or edge cases, but overall is sufficient.

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 coverage is 0%, but the description explains the overall function and mentions shared keys and numeric columns, yet does not individually describe each parameter or their constraints beyond the implicit requirement that col_a/col_b are numeric and exist in their respective tables.

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 computes Pearson correlation between two numeric columns across two datasets, with specific verbs and resources. It distinguishes from sibling tools like query_sql or price_stats by its focused analytical purpose.

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 an illustrative example (correlating wind speed with PM10) that helps understand when to use the tool, but does not explicitly state when not to use it or compare to alternatives like query_sql.

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