get_articleReturn the complete citation-ready Markdown representation of one published Aperture Journal article by slug, including canonical URL, dates, topics, body text, and public images.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish that the operation is read-only, idempotent, and non-destructive. The description adds meaningful behavioral context by specifying the exact return shape ('citation-ready Markdown' with canonical URL, dates, topics, body text, and public images), which is especially valuable because no output schema exists. It does not describe error or not-found behavior, but the main safety-relevant behavior is already covered by the annotations.
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 a single sentence that starts with the action and expected outcome, then packs the key inclusions with no filler or repetition of schema details. Every clause earns 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?
Given only one parameter, strong read-only/idempotent annotations, and no output schema, the description is complete: it names the input, the article scope, and the exact return components. Nothing critical is missing for selecting and invoking the 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 schema's slug property has no attached description (0% coverage), so the description must carry semantic weight. It tells the agent that the slug addresses 'one published Aperture Journal article' and that the result is full Markdown, but it does not explain how to obtain or construct a slug beyond what the regex pattern already encodes. This is adequate for a single well-defined parameter, but not rich.
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 a specific verb ('Return') and resource ('one published Aperture Journal article by slug'), and it enumerates the output contents: canonical URL, dates, topics, body text, and public images. This clearly differentiates it from list_articles, which would return multiple or summary results, even though it does not name the sibling explicitly.
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 clearly establishes the context: call this when you already have an article slug and want the full article. It does not explicitly point to alternatives such as list_articles for cases where the slug is not known, so it stops short of fully prescribing when not to use it.
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 target distinct resource types (product, security, features, articles, networks, release, screens, entry points), with get_/list_ pairs clearly separating single-item retrieval from collection listing. The only mild ambiguity is between get_app_screen and list_app_entry_points, since both describe UI navigation surfaces, though their descriptions keep screen semantics vs entry-point catalogs separate.
The dominant get_<singular> and list_<plural> pattern is clear and predictable for 10 of 12 tools. 'search' and 'fetch' break the noun-oriented pattern and are less descriptive of their distinct roles, but they are common enough verbs that the overall set remains readable.
With 12 tools, the server is well-scoped for a wallet knowledge base: each get tool corresponds to a distinct knowledge category and list tools cover discoverability. No tool feels redundant, and the count is squarely in the appropriate range.
The surface covers the major knowledge domains an agent needs: product identity, security model, features, articles, supported networks, release info, app screens, and entry points, plus search/fetch for open-ended lookup. Minor asymmetries exist (e.g., get_app_screen has no listing counterpart, and list_app_entry_points has no single-item get), but these are workable through search.