Google Ads volume/CPC/competition for 1-20 keywords. $0.04 USDC/call. Paid: calling without a payment payload returns a structured 402 with payment options — that response is the documented behavior, not a failure.
| Name | Required | Description | Default |
|---|---|---|---|
| keywords | Yes | 1-20 keywords. | |
| language_code | No | ISO 639-1. | es |
| location_code | No | DataForSEO location (default Spain). | |
| include_suggestions | No | Add related suggestions. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint=true, but the description adds valuable behavioral context: the $0.04 USDC cost and the structured 402 response when no payment payload is sent, explicitly noting this is documented behavior rather than a failure. This goes beyond what annotations offer.
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 three short sentences that front-load the core purpose, followed by cost and payment behavior. Every sentence adds distinct, useful information with no redundancy.
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?
Despite lacking an output schema, the description covers what data is returned (volume, CPC, competition), the keyword limit, and payment/402 handling. This is sufficient for an agent to select and invoke the tool correctly.
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% with clear descriptions for keywords, language_code, location_code, and include_suggestions. The description itself adds no parameter-level detail beyond the schema, meeting the baseline for high coverage.
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 the tool provides Google Ads volume, CPC, and competition metrics for 1-20 keywords. This specific verb+resource framing distinguishes it from sibling data-fetching tools like fetch_html or json_query.
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 implies use when Google Ads keyword metrics are needed, but does not explicitly state when to use it versus alternatives or reference any sibling tools. No exclusions or alternative suggestions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools overlap in fetching and processing web content (fetch_extract, fetch_html, fetch_metadata, html_to_markdown), which could confuse an agent. However, descriptions clarify output types, so most tools are distinguishable.
Names follow mixed conventions: verb_noun (fetch_html, remove_background), noun_verb (csv_query, rss_parse), and noun_noun (tool_catalog, screenshot_url). Each name is descriptive, but the lack of a consistent pattern makes it harder to guess tool names.
With 17 tools, the count is slightly above the ideal 3-15 range but still manageable. The inclusion of 5 meta-tools (pricing, tool_catalog, task_recipes, memory_snippet, use_tool) inflates the count but serves a discovery purpose.
The toolkit covers a broad range of web and data tasks (fetch, parse, query, convert, image, SEO). Minor gaps exist (e.g., no OCR, no image editing), but use_tool can dynamically access additional tools, mitigating incompleteness.
