analyze_correlation

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool analyzes correlations but lacks details on how it handles missing data, what correlation method is used (e.g., Pearson), whether it's read-only or modifies data, or any performance considerations. This is insufficient for a tool with potential data processing implications.

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 well-structured with a clear purpose statement followed by 'Args' and 'Returns' sections. It's front-loaded and uses bullet-like formatting efficiently. However, the 'Returns' section is vague ('Correlation analysis.'), which slightly reduces conciseness by not adding value beyond what the output schema might provide.

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 3 parameters with 0% schema coverage and an output schema present, the description partially compensates by listing parameters but lacks depth. It doesn't explain the analysis method, data requirements, or error handling. The output schema existence means return values needn't be detailed, but overall completeness is minimal for a statistical tool with no annotations.

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 lists parameters in the 'Args' section, explaining 'columns', 'target', and 'table' with minimal context (e.g., 'Optional list of columns to analyze'). However, it doesn't clarify what 'correlation analysis' entails for these inputs or provide examples, leaving gaps in understanding parameter usage beyond basic definitions.

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: 'Analyze correlations between numeric columns.' It specifies the verb ('analyze') and resource ('numeric columns'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'compare_cities' or 'plot_comparison', which might also involve correlation analysis, so it doesn't reach the highest score.

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 doesn't mention sibling tools or contexts where this analysis is preferred over others, such as 'plot_comparison' for visual correlation or 'describe_table' for general statistics. This leaves the agent without explicit usage direction.

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