List country codes you can pass as country on seo.keyword_metrics.
Returns an array of 2-letter ISO country codes (for example us, gb, de).
Cost = 0 tokens.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| country_codes | No | Supported ISO country codes for keyword metrics. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / $defsRemoved value: -{
- "SeoCountryCodesResponseCountryCodesItem": {
- "additionalProperties": true,
- "properties": {
- "code": {
- "anyOf": [
- {
- "type": "string"
- },
- {
- "type": "null"
- }
- ],
- "default": null,
- "description": "2-letter country code.",
- "title": "Code"
- }
- },
- "title": "SeoCountryCodesResponseCountryCodesItem",
- "type": "object"
- }
-}Output schema / properties / country_codes / anyOfPrevious value: -[
- {
- "items": {
- "$ref": "#/$defs/SeoCountryCodesResponseCountryCodesItem"
- },
- "type": "array"
- },
- {
- "type": "null"
- }
-]New value: +[
+ {
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ },
+ {
+ "type": "null"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It discloses the return type (array of 2-letter ISO codes) and the operational cost (0 tokens). It doesn't mention side effects, but for a list operation none are expected, and the description covers the essential behavioral facts.
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 with no fluff. It front-loads the primary purpose, then covers return format and cost, with each sentence earning its place.
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 zero-parameter list tool with an output schema, the description is complete. It explains what the tool returns, how to use it, and the cost implication. No gaps remain for this simple tool.
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 has zero parameters, so the baseline is 4. The description adds value beyond the schema by explaining the output format and providing examples, which is useful for an agent planning to use the returned codes.
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 lists country codes, specifying the exact use case: passing as the 'country' parameter to seo.keyword_metrics. The verb 'list' and resource 'country codes' are specific, and it distinguishes itself from sibling tools by tying directly to a related SEO tool.
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?
It explicitly states the codes are for use with seo.keyword_metrics, providing clear context on when to invoke this tool. It doesn't mention alternatives, but none exist for this specific purpose, and it adds a practical note about zero token cost.
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.