Get Google Trends interest data for up to 5 keywords. Returns interest over time, geographic breakdown, and related queries.
| Name | Required | Description | Default |
|---|---|---|---|
| geo | No | Country code (e.g. "US", "IN", "GB"). Leave empty for worldwide. | |
| keywords | Yes | Keywords to compare (1-5 terms) | |
| timeRange | No | Time range: "today 3-m", "today 12-m", "today 5-y" | today 12-m |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Given no annotations, the description bears full responsibility for behavioral transparency. It describes outputs (interest over time, geographic breakdown, related queries) but does not disclose performance characteristics, rate limits, data freshness, or any potential limitations beyond the parameter constraints.
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 action and immediately state the key outputs. Every word serves a purpose, with no extraneous 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?
The tool has three parameters and no output schema or annotations. The description adequately outlines the tool's purpose and high-level output categories, but lacks detail on how to interpret the returned data or any examples. It is sufficient for simple use cases but could be more complete for an AI agent.
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 input schema covers all three parameters with descriptions, achieving 100% coverage. The description adds minimal extra meaning by restating the keyword limit (up to 5) and listing what data is returned, but does not elaborate on parameter usage beyond what the schema provides. Per guidelines, 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 the tool's function: 'Get Google Trends interest data for up to 5 keywords.' It specifies the resource (Google Trends) and the action (get data), and distinguishes itself from sibling search tools by focusing on Google Trends specifically.
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 no guidance on when to use this tool versus alternatives like search_google_maps or search_crunchbase. It does not mention when to use or avoid this tool, nor does it compare with other tools.
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.
Most tools target unique data sources or specific actions (e.g., search_zillow vs. get_zillow_property_details are clearly sequential). A few LinkedIn-related tools (find_linkedin_candidates vs. search_linkedin_employees) have overlapping purposes but their descriptions clarify distinct use cases. Overall, confusion is minimal and descriptions resolve ambiguity.
All tools follow a consistent verb_noun pattern in snake_case, using verbs like search, get, find, scrape, analyze, lookup, resolve, and verify. The pattern is predictable across the entire set, making it easy for an agent to infer function from name.
With 32 tools, the server exceeds the 'too many' threshold of 25+. While the broad scope of web data mining justifies some diversity, the count is unwieldy and could overwhelm an agent's selection process. A smaller, more focused set per domain would improve coherence.
The toolset covers a wide range of data retrieval needs: company research, real estate, job listings, academic research, and government records. For a read-only data aggregation service, there are no major lifecycle gaps, though some subdomains like social media scraping only cover Reddit and LinkedIn, missing other platforms. Overall, it is reasonably complete for its stated purpose.
