Ground elevation in metres for a place name. (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 |
|---|---|---|---|
| location | Yes | Place name |
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, requires no signup, and returns elevation in metres, which is useful. However, it does not mention error behavior or rate limits, though for a simple read-only tool this is acceptable.
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: the first states the core purpose, the second adds monetization context. Every word is purposeful and front-loaded, with no fluff.
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 single-parameter tool with no output schema, the description provides sufficient context: it specifies the input type, the unit of the result, and even the cost. It could include an example output or error handling, but overall it is complete enough for successful 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?
The schema already describes the only parameter 'location' as 'Place name' (100% coverage). The description adds no additional parameter semantics beyond restating 'place name', so a baseline score of 3 is appropriate.
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 states 'Ground elevation in metres for a place name' with a specific verb and resource, clearly distinguishing this from siblings like geocode (coordinates) or air_quality. It is concise and unambiguous.
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 clearly implies use for elevation queries and adds operational context (free, no signup). It also directs users to paid_catalogue for a paid API, offering an alternative, though it does not explicitly state when not to use this tool.
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.
Most tools serve clearly distinct purposes: air quality, skill audit, country data, earthquakes, elevation, geocoding, holidays, URL reading, and web search. However, `audit_skill_text` and `audit_skill_url` both audit for malicious behavior, differing only in input type, which could cause slight confusion.
Tool names use a mix of snake_case and descriptive phrases without a strict verb_noun pattern. Some names are verbs (e.g., `geocode`, `audit_skill_text`, `read_url`), while others are nouns (e.g., `air_quality`, `earthquakes`). The naming is readable but inconsistent in style.
With 11 tools, the count is well-scoped for a server that aggregates diverse free data and security services. Each tool serves a distinct and useful function, and the `paid_catalogue` tool properly manages the paid extension without bloating the main set.
The tool set covers a broad range of data types (environment, economic, geographic, security), but for each individual domain, coverage is shallow. For example, only current air quality is provided (no historical data), and skill auditing only returns a scan result (no detailed remediation). The set lacks update, delete, or drill-down operations per domain.