list_releasesList AI model and product releases tracked by AI-Portal, newest first. Filter by category, provider, year, or landmark releases only.
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | Four-digit year, e.g. "2026" | |
| limit | No | Max items to return (default 25, max 100) | |
| category | No | Release category | |
| provider | No | Provider name substring, e.g. "anthropic", "google" | |
| landmark_only | No | Only landmark (major) releases |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'newest first' ordering but does not disclose idempotency, side effects, error handling, or rate limits. For a read-only listing tool, this is minimal disclosure.
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?
A single sentence of 18 words contains all essential information: purpose, ordering, and filter availability. No unnecessary words, and the key action is front-loaded.
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?
The description is adequate for a simple listing tool with optional filters and no output schema. It covers the main use case but omits details about return format, pagination beyond 'limit', and default behavior when filters are empty.
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%, so baseline is 3. The description paraphrases the filtering categories (category, provider, year, landmark_only) but adds no new meaning beyond what the schema already provides with examples.
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 lists AI model and product releases from AI-Portal in reverse chronological order. It distinguishes itself from siblings (news, regulations, glossary) by its specific subject matter and action.
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 indicates filtering options but does not explicitly guide when to use this tool versus siblings or provide exclusions. The sibling tools cover different domains, so the distinction is implicit, but no direct usage guidance is given.
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 serves a distinct purpose: news, regulations, releases, and glossary. There is no functional overlap, making it easy for an agent to select the correct tool.
All tool names follow a consistent verb_noun pattern (get_*, list_*, search_*), with clear and predictable naming.
With 4 tools, the set is compact yet sufficiently covers the core information needs for a GenAI briefing server.
The tools cover news, regulations, releases, and glossary, but lack a tool for generating briefings or summaries, which would be a natural addition for a briefing server.