Get Google Search autocomplete suggestions for a partial query.
Returns the normalized query and an array of suggested search phrases.
Cost = 5 tokens.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Partial search keywords or phrase. |
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Normalized query echoed from the request. | |
| suggestions | No | Suggested search phrases for the query. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
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 adds the return structure and the token cost (5 tokens), which is helpful, but it does not cover potential errors, authentication requirements, or rate limits. For a simple read-only autocomplete tool, this is acceptable but not comprehensive.
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?
Three concise sentences: the first states the purpose, the second the return value, and the third the cost. Every sentence earns its place with no fluff, and the most important information is front-loaded.
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 the tool's low complexity (one parameter, output schema present), the description is sufficient. It covers purpose, input, output, and cost. It could add an example or explain 'normalized query', but the output schema likely fills that gap, making the description complete enough.
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 already provides 100% coverage for the only parameter 'query', describing it as 'Partial search keywords or phrase.' The description reinforces the 'partial query' aspect but does not add substantial meaning beyond the schema, so the baseline score of 3 applies.
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 retrieves Google Search autocomplete suggestions for a partial query, distinguishing it from other search tools like keyword_traffic_insights or url_traffic_insights. It also specifies the return format (normalized query and an array of suggested phrases), making the purpose unmistakable.
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 indicates the tool is for partial queries and lists what it returns, giving clear context for when to use it. However, it does not explicitly mention when not to use it or reference alternative tools like YouTube's search_autocomplete, so it stops short of a 5.
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.
Each tool has a distinct purpose, further clarified by group prefixes and clear descriptions. Within each group, tools perform different operations (e.g., domains.lookup vs. domains.whois vs. domains.rdap) with no ambiguity.
All tools follow a consistent group.tool_name pattern using snake_case. The naming is predictable and uniformly applied across all groups.
78 tools is high, but the server aggregates multiple distinct API domains (11 groups). Each group has a reasonable number of tools, typically under 10, with TikTok having 17. The count reflects breadth, not bloat.
Each domain's tool set covers the primary expected operations (e.g., search, details, reviews, metrics, user info). There are no obvious gaps for read-only analytical use; features like posting are likely out of scope.
