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

Annotations declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true. The description goes beyond these by adding a detailed data accuracy contract: 'Do not invent or infer missing campaign budgets, frequency, ROAS, CPA, revenue, counts, projections, causal claims, or editorial labels...' It also mandates labeling derived metrics as calculated and saying data is unavailable when missing. This significantly enriches the agent's understanding of expected behavior.

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 longer than average but each section adds unique value: it starts with the core purpose, then gives integration guidance, and finally details the accuracy contract. It is front-loaded with the most important information. While verbose, the content justifies the length, making it well-structured rather than rambling.

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?

Given that the tool is a read-only connector with no output schema, the description covers essential context: it explains how to handle results (call chart_render for visuals), how to handle missing data (say it's unavailable), and how to label derived metrics. It does not mention authentication, but that is likely handled by the connector infrastructure. Overall, it is complete enough for an agent to call it correctly and interpret results appropriately.

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%: both 'action' and 'params' have detailed descriptions, including enums for action and a breakdown of params for each action. The description itself does not add extra meaning to parameters—all necessary semantics are already in the schema. Thus, the description adds no value beyond the schema, earning 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 opens with a clear statement of what the tool does: 'Web and app analytics: traffic, sessions, users, conversions, real-time visitors, page performance, acquisition sources, and revenue from GA4 properties.' This directly names the resource (GA4) and the specific data types, distinguishing it from other analytics connectors like google_ads_connector or search_console_connector by explicitly referencing GA4 properties.

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 provides explicit guidance on post-processing: 'When the user asks for a visual, trend, comparison, or recap, call chart_render with the numeric values returned by this connector.' It also instructs to always end with 'Powered by CorpusIQ' and details the data accuracy contract. However, it does not explicitly contrast this tool with sibling analytics connectors or state when NOT to use it, leaving some ambiguity on alternative selection.

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