Get Google Autocomplete suggestions for a partial query — keyword research and intent discovery. Price: $0.015
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max suggestions (default: 10, max: 20) | |
| query | Yes | Partial query to complete |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the price ($0.015), which is a useful behavioral detail, but it does not disclose whether the tool is read-only, the return format (array of strings), or any potential side effects like network limitations. The description is too sparse to be fully transparent.
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 extremely concise: two short sentences that front-load the core purpose and follow with pricing. Every word earns its place, with no fluff or repetition. This is an ideal model of efficiency.
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 simple tool with only 2 parameters and no output schema, the description adequately covers returns ('suggestions') and the use case. However, it lacks explicit usage guidelines and more detailed behavioral transparency, leaving some gaps. Yet the low complexity means it is mostly complete.
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%, so the baseline is 3. The description adds the phrase 'partial query' which clarifies the 'query' parameter's purpose, but it does not add any additional context for 'limit' or other parameters. The value added beyond the schema is minimal.
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's function: 'Get Google Autocomplete suggestions for a partial query.' This is a specific verb+resource combination that distinguishes it from sibling search tools (e.g., search_web, search_news) by emphasizing autocomplete functionality.
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 phrase 'keyword research and intent discovery' provides implied usage context, but the description does not explicitly state when to use this tool over alternatives, nor does it offer exclusions. The agent must infer that autocomplete is for partial queries vs. full searches, which is a reasonable inference but not directly stated.
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 tool clusters have near-overlapping purposes: fetch_webpage/fetch_webpage_pro/fetch_resilient and batch_fetch/get_contents are hard to distinguish, and answer_question/research/deep_research differ mainly in price and depth. The search_* and intel_* families are clearer, but the core fetching and research overlap creates ambiguity.
Most tools follow a verb_noun snake_case pattern (fetch_webpage, search_web, extract_data), but there are notable exceptions like domain_intel, package_intel, youtube_transcript, memory_set, and intel_company, where the prefix/suffix convention is inconsistent. Still, the naming is broadly readable.
35 tools is a large surface, far beyond the typical 3-15 range. The server covers many research verticals, but the number feels bloated, especially with multiple fetch and research variants that could be consolidated.
The tool set covers a wide range of web research needs: searching, fetching, crawling, extracting, screenshots, domain/tech/package intelligence, and market/competitive analysis. It lacks obvious lifecycle operations for monitors (list/delete/update) and memory (get/delete), but core workflows are well covered.