get_paperFetch one AIO paper by id (e.g. "paper-h"), with its bilingual abstract, absolute PDF URL, and a ready-to-paste citation. CC BY 4.0.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Paper id as returned by list_papers, e.g. "paper-h". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnly, idempotent, and non-destructive. The description adds return-value details (abstract, PDF URL, citation) and license (CC BY 4.0), providing useful context beyond 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 concise sentence, front-loaded with the verb 'Fetch', includes an example and key outputs, with no unnecessary words.
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?
With only one parameter and no output schema, the description covers purpose, id format, return contents, and license, making it fully sufficient for an agent to invoke the tool correctly.
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 fully documents the only parameter 'id' with description and example. The description repeats the same example, adding no new semantic value beyond the schema.
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 it fetches one AIO paper by id, lists specific return contents (bilingual abstract, PDF URL, citation), and distinguishes it from siblings like list_papers which would list multiple papers.
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 use when a specific paper id is known and full details are needed, and provides clear context. It doesn't explicitly mention alternatives or when-not-to-use, but the scope is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.