read_cells

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

With no annotations provided, the description discloses key runtime behavior: rows are lists, numbers vs strings are returned, formula=true changes values to formula strings, and sheet defaults by name or 0-based index to the first sheet. This is exactly the behavioral context an agent needs.

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?

Every sentence earns its place: first sentence states purpose and output shape, second explains row types, third explains formula mode, fourth explains sheet selection. No filler or repetition.

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 there is no output schema and no annotations, the description covers return format, value types, formula behavior, and sheet addressing, which is sufficient for safe invocation. Only doc_id semantics and explicit read-only status are left implicit.

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?

Input schema has 0% property descriptions, so the description compensates well for range, formula, and sheet with examples and semantics. It does not explicitly describe doc_id, though the parameter name is reasonably self-evident.

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 uses the specific verb 'Read' with a concrete resource ('cell range') and gives the return shape '{values: [[...]]}', so an agent understands exactly what operation is offered. It does not explicitly name sibling tools like set_cells or get_text, but the cell-range focus makes it distinguishable.

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?

It gives clear context for when to use the tool (reading a cell range from a spreadsheet) and useful conditional guidance ('Set formula=true ... useful for inspecting an existing model'). It does not explicitly list alternatives or exclusion cases, so it stops short of 5.

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