List the newest AI models with sourced facts (release date, context window, modalities, licence, price, source URL, last-verified date). Newest first.
| Name | Required | Description | Default |
|---|---|---|---|
| group | No | Use group, e.g. coding, open-weights, local, small-fast. | |
| limit | No | ||
| vendor | No | Vendor name, case-insensitive, e.g. Anthropic. | |
| category | No | Model category. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds genuine context beyond that: results are sourced facts with provenance and a 'last-verified date', and ordering is 'Newest first' — both material to how an agent should trust and present the data.
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?
Two sentences, zero waste. The returned-field list and sort order are packed into one clause and the leading verb carries the purpose immediately.
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 no output schema, the description usefully enumerates the return fields, and it discloses sort order and data provenance. The remaining gap is filter semantics — how group/vendor/category interact or whether they combine — which an agent would have to discover empirically.
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 75%; group, vendor, and category are documented in the schema, while limit has only a default/bounds. The description says nothing about any parameter, so it neither compensates for the undocumented limit semantics nor enriches the filter behavior. Baseline 3 is correct at this coverage level.
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?
States a specific verb and resource ('List the newest AI models') and enumerates the fields returned, so the agent knows exactly what it gets back. It does not, however, distinguish itself from the sibling latest_releases, which reads as a near-synonym.
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?
No guidance on when to use this versus latest_releases, get_model, or compare_tools — the most likely confusion points given the sibling set. The agent must infer that this is a browse/list tool and the others are lookup/compare tools.
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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