keyword_research

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

With no annotations, the description fully discloses error conditions (empty/excessive keywords, API unavailability), side effects (none), and cost ($0.04 USDC pay-per-call, no free first call). This is exceptionally transparent for an AI agent.

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 concise yet comprehensive, front-loading the action and output, and including error handling, side effects, cost, and use cases in a single paragraph. No unnecessary information.

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 there is no output schema, the description fully explains the return JSON structure (specific metrics). It also covers all error scenarios and side effects, making it complete for an AI agent to understand usage.

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 the description adds little beyond the schema, except for noting that include_suggestions affects latency. Baseline 3 is appropriate.

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 it queries Google Ads data via DataForSEO for 1-20 keywords and lists the specific metrics returned (search volume, CPC, competition, trend, suggestions). It is distinct from sibling tools which are mostly utilities like encoding, fetching, or finance tools.

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 mentions ideal use cases (SEO audits, content planning, PPC budget estimation) but does not explicitly state when not to use or list alternatives. However, no sibling tools overlap with keyword research, so the context is clear.

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