list_result_tables

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

Annotations already cover read-only, idempotent, and closed-world, so the safety profile is handled. The description adds genuinely new behavioral context: no NX call is made (no live model round-trip) and each category states what NX must have done before its tables produce data — a prerequisite signal an agent would otherwise have to discover by trial.

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?

Front-loaded purpose sentence followed by a compact category block and a search note — no filler. The sentence 'Each category says what NX needs before its tables have data' is slightly oblique about what the agent will actually see, but the density is justified given four enum values to explain.

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 no output schema and an undocumented input schema, the description supplies the missing shape: a categorized list of table types, filterable by text and category, requiring no NX call. It does not spell out filtering precedence when both search and category are supplied, a minor gap for a two-parameter discovery tool.

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 must carry the burden. It does: 'search' is defined as case-insensitive text matched against both the type and its name, with concrete examples ('reaction', 'beam', 'drift'), and each 'category' enum value is explained in terms of the table families it contains.

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?

Opens with a specific verb+resource: listing the result/report table types that the sibling tool result_table accepts, grouped by category. The four named categories (model, analysis, story, design) with concrete example tables make it immediately distinguishable from siblings like list_tables or describe_table.

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?

Strong implied usage: this is the discovery step that tells you which values result_table will accept, and it explicitly states 'No NX call is made', so an agent knows it is cheap and safe to call first. It does not explicitly compare itself to the sibling list_tables or say when that alternative is preferable, so it falls short of full when/when-not guidance.

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