List countries
list_countriesList covered countries and how many cities each one has.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
list_countriesList covered countries and how many cities each one has.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
Annotations already declare readOnlyHint=true, so the agent knows it is safe. The description adds that the response includes city counts, providing useful behavioral detail without overwhelming. It could mention more about what is returned or potential limitations.
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?
A single, dense sentence packs the essential meaning. It is perfectly sized and front-loaded with the action verb.
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 list tool with no parameters, the description is nearly complete. It explains the tool's result shape (countries + city counts). A tiny bit more context about the response format could be added, but it's not necessary.
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?
With no parameters, this dimension is largely moot. Baseline of 4 is appropriate since the description confirms it requires no arguments and provides meaning for the output content.
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 uses a specific verb ('List') and resource ('countries'), with the extra detail 'how many cities each one has', which clearly communicates the tool's function and output shape. It does not explicitly differentiate from siblings, but its meaning is clear.
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 implies its use for getting a country list with city counts, but it does not specify when to prefer this over sibling tools or provide exclusions. It offers implied usage context by being distinct from get_catalog_overview, get_city_guide, and search_cities.
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 serves a distinct purpose: overview statistics, city-specific details, country listing, and city search. There is no functional overlap or ambiguity between them.
All tool names follow a consistent verb_noun pattern with clear action prefixes (get, list, search). The naming is uniform and predictable.
With only 4 tools, the server is tightly scoped for a catalog domain. Each tool provides essential functionality without unnecessary bloat or gaps.
The server covers all key read operations for a catalog: overview, detailed city guides, country coverage, and search. The workflow from discovery to detail is complete.