explain_formula

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

No annotations are provided, so the description carries the full burden. It mentions the tool explains formulas in plain English but does not disclose behavioral traits such as whether it uses external AI services (implied by the provider parameter), potential rate limits, error handling for invalid formulas, or output format. This leaves significant gaps in understanding how the tool behaves.

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 a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part earns its place by clearly stating the tool's function, making it easy to understand at a glance.

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 complexity of explaining formulas (which may involve AI providers) and the lack of annotations and output schema, the description is incomplete. It does not address how explanations are generated, what the output looks like, or any limitations, which are crucial for an AI agent to use the tool effectively.

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 100%, so the schema already documents both parameters thoroughly. The description does not add meaning beyond the schema, as it does not explain parameter interactions, default behaviors for the optional provider, or examples of formula formats. Baseline 3 is appropriate given the high schema coverage.

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 specific action ('explain') and resource ('Excel formula'), with the qualifier 'in plain English' distinguishing it from sibling tools like evaluate_formula (which likely computes results) or parse_natural_language (which might convert text to formulas). It directly answers what the tool does without being tautological.

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 implies usage for understanding Excel formulas, but it does not explicitly state when to use this tool versus alternatives like evaluate_formula (for computation) or other analysis tools. No exclusions or prerequisites are mentioned, leaving the context somewhat open-ended.

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