update_features

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

With no annotations, the description carries the full burden of behavioral disclosure. It clearly states that the update is a bulk operation applied uniformly to matching rows, and it explicitly warns about the destructive implication of an empty where_clause ('be deliberate about this'). It stops short of mentioning irreversibility, permission requirements, or field existence checks, but the core behavioral profile and main risk are disclosed.

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 and efficient. The main behavior statement, sibling distinction, and caution are front-loaded, followed by a compact Args section that maps directly to parameters. No redundant or filler sentences; every sentence earns its place.

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 tool's complexity (3 required params, nested object updates, no annotations), the description is largely complete: it covers purpose, parameter semantics, the key safety caveat, and the main alternative. An output schema exists, so return value details are not mandatory. Minor omissions like field existence validation or exact behavior when an update fails would improve completeness, but an agent can invoke this tool correctly from the description alone.

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 fully compensates with a dedicated Args block. Each parameter gets meaningful semantic detail: layer requires an exact name as shown in the Contents pane, where_clause gets a SQL example and an empty-string convention, and updates gets a concrete dictionary example. This adds substantial value beyond the bare schema types.

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: 'Update attribute values on features matching a WHERE clause.' It immediately clarifies the bulk nature ('Sets the same field value(s) on every matching row') and distinguishes itself from execute_python for per-row updates. This clearly separates it from sibling tools like execute_python and select_by_attribute.

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?

Usage guidance is explicit and actionable. It states when to use the tool (bulk update over a WHERE clause) and when not to use it ('For per-row computed values ... use execute_python with an UpdateCursor instead'). It also warns about the empty where_clause case, advising caution. This meets the when/when-not/alternatives bar.

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