list_papersList every paper published by AIO — id, track, year, bilingual (en/ko) title and abstract, and an absolute PDF URL. All papers are CC BY 4.0; cite as "AIO — AI Integrity Organization, https://aioq.org, CC BY 4.0".
| Name | Required | Description | Default |
|---|---|---|---|
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive hints. The description adds meaningful behavior beyond this: it specifies the exact return fields, that all papers are included, and licensing/citation requirements. This is useful context the annotations do not provide.
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, front-loads the core action and output, then adds licensing/citation detail. Every sentence contributes information without redundancy, making it highly efficient.
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 zero-parameter list tool with no output schema, the description fully specifies the return contents (id, track, year, bilingual title/abstract, PDF URL) and adds citation instructions. It is complete for the tool's purpose; no other behavioral expectations are needed.
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 tool has zero parameters, so baseline is 4. The description does not need to add parameter semantics, and the schema coverage is 100% with an empty structure. No additional parameter info is required.
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 uses the specific verb 'List' with resource 'every paper published by AIO' and enumerates exact output fields (id, track, year, bilingual title/abstract, PDF URL), making the purpose unambiguous. The phrase 'every paper' distinguishes it from sibling get_paper, which presumably retrieves a single paper.
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 usage (when you need all papers) but does not explicitly state when to use this tool over alternatives like get_paper. There is no mention of exclusions or prerequisites, only an implicit 'use for complete list' context.
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 targets a distinct resource or action: fetching items vs. distributions, listing vs. fetching specific entities, and distinct submission endpoints for benchmark, eval, and RFC comments. Even the two 'get items' tools (bench vs. eval) are clearly differentiated by their descriptions and use cases.
All tool names follow a consistent verb_noun pattern with lowercase and underscores. Verbs are grouped by action type (get_, list_, submit_, start_, register_, verify_, search_), and the patterns are predictable and uniform across the set.
At 17 tools, the set is slightly above the ideal 3-15 range but each tool serves a distinct purpose. The breadth of the domain (papers, RFCs, standards packs, benchmarks, evaluation, certification, vocabulary, search) justifies the count, and no tool feels redundant.
The tool surface covers the major workflows: listing and fetching resources, starting and submitting evaluation attempts, registering and verifying certifications, and submitting benchmark/RFC responses. Minor gaps exist, such as no direct tool to cancel an evaluation attempt or list a specific benchmark run by id, but these are edge cases and the core lifecycle is well-covered.