correlation_matrix

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

The annotations include readOnlyHint: true, and the description adds valuable nuance: 'Read-only unless output_sheet/output_file is set.' This transparently discloses the conditional side effect and goes beyond the annotation's binary hint. It also describes the return structure, which is helpful given the lack of an output schema.

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 front-loaded with the core purpose, then uses a clear structured format for Args, Returns, and Notes. Every line adds value, and the parameter list is appropriately terse given the number of parameters.

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?

Despite having no output schema, the description fully specifies the return format. It covers the read-only/write conditional, parameter semantics, and scope (numeric columns only). For a tool of this complexity, it is remarkably complete and leaves minimal ambiguity.

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 0% description coverage, meaning the description is the sole source for parameter meaning. It thoroughly explains each of the six parameters (file_path, sheet_name, columns, output_sheet, output_file, header_row) with concise, accurate semantics, including defaults and the 1-based header_row note.

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 opens with a specific verb and resource: 'Compute a Pearson correlation matrix for numeric columns.' This clearly distinguishes it from sibling tools like column_statistics or run_regression, which compute different statistical outputs.

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 clear context for what the tool does but does not explicitly state when to use it over alternatives or mention exclusions. For example, it does not say 'use this when you need pairwise correlations among numeric columns, not for single-column summaries.' This leaves the agent to infer usage from the function name and sibling list.

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