Create Lookup Table

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

Goes well beyond the annotations: discloses that rules only see an active version, that activation publishes to MAS and CAS in 'usually seconds', and that wait_seconds governs how long to block. It also surfaces non-obvious validation constraints (key texts up to 256 chars, values up to 512, numbers stored as text, keys unique, empty value behaves like a missing key) that no annotation or schema field conveys.

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-loads the core purpose, then layers activation semantics and constraints. Dense but every paragraph carries information; the embedded rule-JSON examples for `lookup` and `lookupValue` are the only portion that could arguably be trimmed for an agent that already knows the ruleset format.

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?

For an 8-parameter creation tool with annotations and an output schema, this covers purpose, activation lifecycle, and data constraints well; return values need not be explained. It omits failure behavior — e.g. what happens on duplicate keys or name collisions — which an agent creating assets would benefit from knowing.

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 coverage is 100%, so baseline is 3, and the description earns above that by adding real meaning: it explains the runtime consequence of `activate` (MAS/CAS publication) and puts hard limits on the shapes accepted by `entries` and the key/value content. The remaining parameters (folder, key_label, value_label, description) are left entirely to the schema.

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 and resource ('Create a SAS Intelligent Decisioning lookup table') and immediately defines what the resource is — keys plus a stored value per key. This distinguishes it clearly from sibling creators like create_value_list, create_business_ruleset, or create_segmentation_tree without needing to inspect any schema.

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?

Explicitly routes on the activation decision: pass `activate` to publish immediately, or 'call activate_lookup_table once the entries are final' — naming the concrete sibling alternative. It does not address the update path (update_lookup_table) or when to prefer a value list instead, so it stops short of full when/when-not coverage.

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