How the published artefact size has moved over time. Returns the readings taken, not a verdict. Requires a key; see https://sighttrue.com/pricing.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Package name as the registry spells it. | |
| registry | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses that the tool returns raw readings, not a verdict, and that it requires an API key. However, it omits details about output structure (e.g., does it include timestamps? bytes?), limits, or pagination. This is partial transparency, enough to set expectations but not fully comprehensive.
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-loaded with the core purpose and followed by a key clarification ('not a verdict') and an auth requirement. Every sentence adds value—no fluff, no repetition of schema details. It is highly concise and well-structured.
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 annotations and no output schema, the description needs to cover more ground. It explains the basic operation and output nature, but lacks details on result format, any date range or filtering, and the exact meaning of 'registry'. It is adequate for a simple tool but leaves gaps that could cause incorrect usage, especially given the wealth of sibling tools (e.g., list_readings, model_price_history) that might overlap.
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 50%—only 'name' is described ('Package name as the registry spells it.'), while 'registry' has no description. The tool description adds no parameter-specific detail, leaving 'registry' ambiguous (e.g., which registry? npm, PyPI?). The description mentions 'published artefact size' but does not clarify how the parameters map to that context, failing to compensate for the coverage gap.
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 identifies the resource (published artefact size over time) and the action (returns readings/history). It distinguishes itself from sibling tools by explicitly stating 'Returns the readings taken, not a verdict', positioning it as a raw data tool versus assessment tools. However, it lacks an explicit verb like 'get' or 'list', relying on the noun phrase 'How...has moved'.
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 phrase 'not a verdict' implies use when raw historical data is needed rather than a conclusion, offering some guidance. It also notes 'Requires a key' as a prerequisite. But it does not explicitly name alternative tools (e.g., check_package, advisory_severity) or define clear when-to-use/when-not-to-use scenarios, leaving usage largely implied.
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 query type: package status, stack review, provider incidents, model pricing, watchlist changes, etc. Even similar-sounding tools like check_package and check_stack are clearly differentiated by granularity (single package vs. whole manifest). The descriptions further remove ambiguity.
All names use lowercase snake_case, but the pattern is mixed: some are imperative verb_noun (check_package, find_model, watch_add) while many are noun phrases (advisory_severity, provider_incidents, runtime_deadlines). This is readable but not a consistent verb_noun style, so there is noticeable inconsistency.
At 31 tools, the count exceeds the 'too many' threshold (25+). While the domain is broad, the agent must navigate a large surface with many similarly scoped utilities, making selection harder. A more consolidated set (e.g., grouping related readings) would improve appropriateness.
The tool surface covers a wide range of supply-chain intelligence: package advisories, provider status, model pricing, runtime EOL, and watchlist changes. The only notable gap is lifecycle management for the private watchlist (e.g., no watch_remove or watch_list), but the overall coverage is strong.