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

Annotations already declare readOnly/idempotent/openWorld/non-destructive, so the safety profile is covered. The description adds genuinely non-structured behavior: the 15-minute provenance_token receipt window usable on any replica, source_path mapping, and the consequence that chart_render marks values unverified_model_data without a receipt. Rate limits, pagination, and auth flow for the connector itself are not disclosed.

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?

Purpose and platform scope are front-loaded in the first sentence, followed by routing and contract rules. The data-accuracy contract is long and partly generic policy text, but each paragraph maps to a distinct obligation (routing, provenance, attribution footer, no-invention), so little is pure filler.

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 read-only, multi-action connector with no output schema, the description covers the metric fields returned, downstream routing, and provenance/attribution obligations. The per-action return shapes are handled by the enum descriptions in the schema, leaving only minor gaps (pagination, error behavior) unstated.

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% and the enum's action values carry their own per-action parameter documentation, so the schema does the heavy lifting. The description adds no additional parameter meaning (no date formats, limit bounds, or id conventions), so the baseline 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?

Names a specific platform and resource set: 'LinkedIn Marketing API (Ads): sponsored ad accounts, campaigns, creatives, and daily performance analytics' with concrete metric fields. An agent can distinguish this from meta_ads_connector, google_ads_connector, and cross_source_ads_connector purely by the LinkedIn B2B paid-social framing.

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?

Gives clear usage context ('B2B paid-social reporting, LinkedIn campaign performance, and account-level ad spend') and explicitly routes downstream visualization to chart_render when the user wants a visual, trend, comparison, or recap. It does not, however, state when NOT to use it versus the other platform ad connectors, so the sibling selection remains implicit.

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