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

The annotations already declare readOnlyHint=true, so no safety contradiction. The description adds valuable behavioral detail beyond that: accounts can carry multiple tags and therefore appear in multiple groups with counts that do not add to the total, and include_views is tied to reading each account's latest stats snapshot with a performance tradeoff for large rosters. This is exactly the kind of non-obvious behavior an agent needs.

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?

Three sentences with no filler: the core capability, the canonical use case, and the most important caveat (non-additive group counts) all appear up front. Every sentence earns its place.

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, the description carries the burden of explaining the return value, and it does: counts per group, connection-status breakdown, and aggregated metrics including followers, average views, breakthrough posts, and 24h/7d views. It also covers the toggle for listing individual accounts and the multi-tag caveat, making it complete for a 7-parameter read-only aggregation 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 100%, so the input schema already documents every parameter's meaning and default. The description adds useful context about tag overlap causing duplicate grouping, but it does not materially extend the per-parameter semantics beyond what the schema provides. The baseline of 3 applies.

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 uses a specific verb ('Roll up') with a clear resource ('the org's own connected accounts (My Accounts)') and the exact grouping dimensions ('by tag, category or platform'). It also gives the canonical use-case query, 'how are the accounts tagged X doing', which distinguishes it from list_my_accounts and get_my_account_stats.

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 identifies the tool as 'the tool to answer...' a specific class of aggregation questions, giving clear context for when it is appropriate. It does not explicitly name alternatives or state when not to use it, but the use-case framing is strong enough to guide selection among the many sibling tools.

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