Resolve a place name to coordinates, country, admin region, timezone, elevation and population. (Free. This server also sells a paid API — call paid_catalogue for the routes and prices; x402 over USDC on Base, no signup.)
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Place name to resolve | |
| count | No | 1-20 candidates (default 5) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool is 'Free' and mentions a paid API, but does not reveal rate limits, response structure, behavior for invalid place names, or the fact that it can return multiple candidates via the count parameter. This is minimal transparency for a geocoding service.
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 two sentences, with the main purpose in the first sentence and a relevant note about the paid API in the second. Every sentence contributes value, and it is appropriately concise.
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 core purpose and output fields are covered, which is helpful given no output schema. However, the description omits the multi-candidate behavior (count parameter) and any error-handling or rate-limit information, which would be expected for a geocoding tool with no annotations.
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 coverage is 100% for both parameters, so the baseline is 3. The description adds context by listing outputs, which indirectly clarifies what 'name' should accept, but it does not elaborate on the count parameter beyond the schema's description. No additional semantics are needed beyond what the schema provides.
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 specific verb 'Resolve' and the resource 'place name', and enumerates the outputs (coordinates, country, admin region, timezone, elevation, population). This distinguishes it from sibling tools like elevation or web_search, which have different purposes.
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 use for converting place names to geographic data but does not explicitly state when to prefer this tool over alternatives. It does mention calling paid_catalogue for the paid API, giving a hint about cost-based alternatives, but no direct comparison with other sibling 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.
Each tool has a clear, distinct purpose (air quality, country indicators, earthquakes, elevation, geocode, holidays, URL reading, web search, catalogue). The only overlap is that geocode already returns elevation, making the separate elevation tool redundant and potentially confusing.
All tool names follow a consistent lowercase snake_case pattern, mixing noun-based names (earthquakes, elevation) and verb_noun names (read_url, web_search). The style is uniform across the set, so naming is predictable and readable.
Nine tools is a reasonable count for a general-purpose data/web utility server. However, the set feels like a random assortment of free APIs rather than a coherent scoped collection, which slightly undermines the sense of appropriateness.
The server name 'tokenguard' implies a focus on token safety and blockchain intelligence, but the free tools cover unrelated domains (air quality, geocoding, holidays, web). The actual token-safety features are only mentioned in the paid_catalogue and are not exposed as MCP tools, leaving a significant gap between the server's apparent purpose and its actual capabilities.