analyze_formula_dependencies

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

The description mentions detecting circular references and output reduction, giving some behavioral insight. However, without annotations, it fails to disclose whether the tool modifies the spreadsheet, permissions required, or behavior on large datasets, leaving significant gaps.

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?

Single sentence with no extraneous words, front-loading the core purpose. Highly concise.

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?

With 4 parameters, no output schema, and sibling tools covering similar analysis, the description is too minimal. It omits output format, the meaning of '99% output reduction', and how results are structured, making it incomplete for effective use.

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 100% with descriptions for all parameters. The tool description adds no extra meaning beyond the schema, meeting the baseline for adequate parameter understanding.

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 analyzes formula dependencies and detects circular references, which distinguishes it from siblings like 'detect_formula_errors' and 'optimize_formula_performance'. However, the '99% output reduction' is vague and not explained, slightly reducing clarity.

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 on when to use this tool versus alternatives (e.g., 'detect_formula_errors' or 'optimize_formula_performance'). Lacks context on prerequisites or use cases, leaving the agent to infer from the tool name alone.

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