list_articlesList published Aperture Journal articles with canonical HTML and Markdown URLs. Optionally filter by category and set a bounded result limit.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| category | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, idempotent, and non-destructive, so the description only needs to add context. It does so by specifying that only published articles are returned, that results include canonical HTML and Markdown URLs, and that the result count is bounded. Minor gaps like ordering and pagination are not addressed, but they are secondary to the safety profile already covered by 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 with no filler. It front-loads the resource and output, then states the two optional behaviors concisely.
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 read-only list tool with two optional parameters and no nested objects, the description covers the resource, output URLs, filtering, and bounded limit. Exact output field details are absent, but they are not necessary to invoke the tool correctly, and the annotations handle side-effect and safety expectations.
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 0%, so the description must compensate for the bare parameter names. It does this by explaining that 'category' is a filter and 'limit' sets a bounded result count. It does not enumerate possible category values, but the schema already provides limit's numeric range and default.
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 opens with a specific verb 'List' and a specific resource, 'published Aperture Journal articles', and names the key output detail (canonical HTML and Markdown URLs). This clearly distinguishes it from siblings such as get_article, which implies single-article retrieval.
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 gives clear context: this is a bulk list operation over published articles, with optional category filtering and a bounded limit. It does not explicitly mention alternatives like get_article or search, but the list-versus-single distinction is evident and no exclusion guidance is critical here.
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.