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

Beyond the sparse annotations, the description discloses two important behaviors: overwriting same-key entries and global propagation across customers. It does not mention side effects like audit logs or return values, but the key mutational semantics are transparent enough for an agent to reason about consequences.

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?

Two short sentences with no filler. The primary action and overwrite behavior are front-loaded, and the global-propagation detail is placed second without unnecessary elaboration.

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 a four-parameter mutation tool with a complete input schema and no output schema, the description covers the essential semantics: upsert behavior and global scope implications. It could additionally clarify customer-scope visibility or return behavior, but these are not critical gaps given the schema richness.

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 parameters are already documented. The description adds meaning beyond the schema by explaining behavioral implications of the scope parameter: for GLOBAL datasets, value updates take effect across all customers. This clarifies an aspect the schema enumeration alone does not convey.

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 operation: 'Add a (key, value) entry to a dataset.' It further differentiates this from a plain insert by noting that existing entries with the same key are overwritten, which is the core distinguishing behavior relative to remove_dataset_entry and list_dataset_entries.

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 overwrite semantics imply this tool is suitable for both inserting and updating entries, but the description does not explicitly state when to use this tool versus alternatives like remove_dataset_entry or list_dataset_entries. The usage context is implied rather than explicitly routed with 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.