v1_wikipediaWikipedia summary: Lead summary/extract for a Wikipedia page. Source: Wikipedia REST. $0.005 per call · GET /v1/wikipedia
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | Wiki language (default en). Example: 'en'. | |
| title | Yes | Page title. Example: 'Cloudflare'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate a safe, read-only, idempotent operation, and the description does not contradict them. The description adds useful operational context such as the Wikipedia REST source, endpoint path, and per-call cost, though it does not describe response format or failure behavior.
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 compact and front-loaded with the core purpose, followed by source, cost, and endpoint details. It is slightly repetitive with 'Wikipedia' appearing multiple times, but no sentence is wastefully long.
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 two-parameter read-only tool with strong annotations, the description covers purpose, source, cost, and endpoint. It does not specify the response format, but the phrase 'Lead summary/extract' gives a reasonable expectation, and the schema covers the parameters well.
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 100%, with both parameters already explained with examples: 'title' for the page title and 'lang' for the language with a default. The description adds no additional parameter-level meaning, so the 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 clearly states the tool returns the lead summary/extract for a Wikipedia page, which is a specific verb-plus-resource action. It is easily distinguished from all sibling tools, which cover separate domains like weather, DNS, or country data.
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 makes it clear the tool is for obtaining Wikipedia page summaries by page title. It does not explicitly say when not to use it, but none of the sibling tools are close enough to create confusion, so the context is sufficient.
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.
Every tool targets a distinct resource or operation—geocoding, weather, DNS, VIN, stock quotes, etc.—with no meaningful overlap in purpose. Even the location- and finance-related tools are clearly separated by their descriptions.
All tools follow the same v1_<resource>[_modifier] snake_case pattern, such as v1_air_quality, v1_reverse_geocode, and v1_validate_email. Although the names are not verb-based, the convention is perfectly consistent across all 31 tools.
31 tools exceeds the 25+ threshold and creates a heavy selection burden for agents, even though the server's stated purpose is broad. Many endpoints are small single-purpose lookups that could be grouped into fewer combined tools without losing clarity.
As a general public-data utility, the set covers a wide range of common lookup categories: location, weather, finance, legal, health, business, internet, and reference data. It has minor gaps like historical financial time series or phone-number validation, but no obvious dead ends since all tools are self-contained read-only lookups.