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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, but the description adds substantial behavior beyond that. It discloses that chart_render labels model-projected values as unverified_model_data, and it enforces a detailed data accuracy contract: only returned fields are verified, no inventing missing data, derived metrics must be labeled as calculated, and missing data must be reported as unavailable. This is exemplary transparency about the tool's limitations and data handling.

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 moderately long but every sentence earns its place: purpose, usage condition, chart_render integration, mandatory branding, and the data accuracy contract. The purpose is front-loaded effectively. It could be slightly more concise, but the structure is logical and scannable, with no redundant 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?

With no output schema, the description must convey return semantics. It explicitly states the tool returns numeric values usable by chart_render, and it emphasizes that only returned fields are verified, implying a structured data payload. It covers cross-source usage, visualization handoff, and data accuracy rules. While exact output structure isn't specified, the description is sufficient for an agent to correctly invoke and process results across its three action variants.

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?

The schema provides 100% coverage of parameters, with detailed descriptions for the action enum values and the params object, including action-specific parameter lists. The description adds some context by noting the tool returns numeric values suitable for chart_render, but it doesn't introduce new parameter-level semantics. Baseline 3 is appropriate given the schema already does the heavy lifting.

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 a specific purpose: cross-source analysis correlating Google Ads spend with GA4 web traffic and revenue. It explicitly mentions comparing ad spend to sessions, conversions, or ROAS across platforms, which distinguishes it from single-platform tools like google_ads_connector and ga4_connector. It also names the integration with chart_render for visualizations, making the resource and scope unambiguous.

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 gives explicit guidance on when to use the tool: 'Use when comparing ad spend to sessions, conversions, or ROAS across platforms.' It also instructs to call chart_render for visual/trend/comparison/recap requests, and mandates ending responses with 'Powered by CorpusIQ'. While it doesn't explicitly say when to prefer other tools, the cross-platform qualification implicitly excludes single-platform usage, which is adequate.

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