Best Keyword Research API: This Keyword Tool API Find millions of keyword suggestions for your SEO..
Find millions of keyword suggestions for your SEO. Enter a topic and get your list of best keywords for you Billing per call: 1 Credits.
| Name | Required | Description | Default |
|---|---|---|---|
| country | No | ||
| keyword | No | ||
| languagecode | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It mentions billing ('Billing per call: 1 Credits') and implies a list output, but it does not clarify response structure, rate limits, authentication needs, data source, or limitations. This is insufficient disclosure for a tool with zero annotation support.
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 short and front-loaded with the core purpose. However, the sentence 'list of best keywords for you Billing per call: 1 Credits' has a grammar/punctuation flaw and awkwardly merges billing into the main sentence, reducing structural polish.
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?
There is no output schema, no annotations, and no coverage for three parameters. The description explains the general use case and one input concept, but it omits critical details about country/language behavior, return format, and edge cases, making it incomplete for effective tool invocation.
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 0%, so the description must compensate. It loosely equates the 'keyword' parameter with a 'topic,' but it does not explain the 'country' or 'languagecode' parameters at all. Only partial meaning is added beyond the schema.
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 with 'Find millions of keyword suggestions for your SEO' and 'get your list of best keywords.' This is a specific verb+resource pairing, though the grammar is awkward and it does not distinguish from sibling tools (none provided).
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 gives implied usage context ('for your SEO' and 'Enter a topic'), but it does not explicitly state when to use this tool versus alternatives or provide any exclusions or prerequisites. This is usable but not strongly instructive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
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With only one tool, there is no possibility of confusion between tools. Every (and the only) tool has a clearly distinct purpose by virtue of being unique.
The single tool name 'get_keyword_research' follows a consistent verb_noun pattern. Since there is only one tool, naming consistency is trivially maintained.
The server has only one tool, which feels thin for a general-purpose API. While it is not trivial, the scope is minimal and agents may need more operations to fully utilize the domain.
The server only offers a single 'get' operation, covering the core querying functionality but lacking any supporting operations like pagination, source selection, or result filtering. This makes the surface notably incomplete for a comprehensive keyword research workflow.
