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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds valuable behavioral context: member_quantities are resolved to article_number/name for readability, an empty observations array is returned for groups with no logs (not an error), and the operation requires an MCP API key and operates only within the team's Library. No contradiction with annotations.

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 detailed yet tightly packed. Each sentence serves a purpose: purpose, usage, readability, empty-array behavior, and auth/scope. It is front-loaded with the core action and no fluff. Despite its length, it is efficiently structured.

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 list operation with no output schema, the description covers the key aspects: what is returned (observations with resolved article info), the edge case (empty array), and scope. It also ties the data to the broader resolve flow. Minor omission: it doesn't enumerate all fields in each observation, but the mention of member_quantities resolution provides enough context. Would benefit from an explicit note on ordering or pagination, but not critical.

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 parameter descriptions already exist. The description adds team-scoping context for group_name_query ('within that team's own Library -- never across teams'), which clarifies its semantics beyond the schema's generic 'Free-text search.' This incremental information raises the score above the baseline 3.

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 function: 'Lists the real, observed consolidation packs logged against a consolidation group in your team's Library.' It specifies the action (lists), resource (consolidation packs), and scope (logged against a consolidation group). It also differentiates from siblings by referencing the ground-truth data that resolve_quantities/resolve_prepack_quantities learn from and explicitly names log_pack_observation as the counterpart for adding new observations.

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?

Explicit guidance is provided: 'Use this to see what's already been logged for a group before deciding whether a new observation (log_pack_observation) is needed, or to review a group's own observation history.' It names the alternative tool, clarifies the context, and covers edge behavior (empty array is valid). No ambiguity remains about when to invoke this tool.

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