get_mandatory_fields

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

Annotations already declare readOnlyHint=true, so the agent knows this is a safe read operation. The description adds behavioral context: it lists fields and their applicability conditions, and emphasizes its role in error prevention. It doesn't describe the exact return format, but for a simple list operation, the disclosure is sufficient given the annotation coverage.

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 two sentences, front-loaded with the core purpose, then a practical usage note. Every word earns its place; there is no redundancy or filler. It is concise yet complete.

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?

The tool has a single parameter and no output schema. The description states it returns a list of mandatory fields and their application conditions, which gives the agent a sufficient mental model for invocation. While the exact JSON structure isn't specified, the description's clarity about the content compensates for the missing output schema, making it adequate for a tool of this simplicity.

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 schema already provides 100% coverage for the single 'table' parameter with a clear example. The description mentions 'on a table' but adds no additional semantic detail beyond what the schema offers. This meets the baseline for high schema coverage, but no extra value is added.

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's purpose: it lists mandatory fields enforced by ServiceNow Data Policies on a table, including when they apply. This is a specific verb-resource pairing that distinguishes it from sibling tools like get_table_schema (which lists schema fields) and query_records (which retrieves data). The purpose is unambiguous and actionable.

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 explicitly advises calling this tool before creating or updating a record to collect required fields up front, preventing a specific error. This gives clear when-to-use guidance. However, it does not mention alternative tools (e.g., get_table_schema) or conditions when this tool would be unnecessary, so it's slightly below a perfect score.

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