help_availability
For a technology tag: questions asked against questions answered, over time. Requires a key; see https://sighttrue.com/pricing.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| tag | Yes |
For a technology tag: questions asked against questions answered, over time. Requires a key; see https://sighttrue.com/pricing.
| Name | Required | Description | Default |
|---|---|---|---|
| tag | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that a key is required (authentication) and that the metric is time-based. It does not mention return format, pagination, or error behavior, leaving some behavioral uncertainty; however, for a simple read-only metric tool this is acceptable.
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?
One sentence plus a URL, no filler. The description is front-loaded with the core functionality and then the key requirement.
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?
No output schema exists, and the description does not specify the shape or interpretation of the result (e.g., a ratio, a series of data points). It also omits any error conditions or what happens with an invalid tag, so the description is not fully self-contained.
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 only provides 'tag' as a string; the description clarifies it refers to a technology tag, which adds meaning. It does not provide example values, accepted formats, or context on how the tag should be specified (e.g., exact name, case sensitivity).
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 computes a ratio of questions asked to questions answered for a technology tag over time. It distinguishes itself from sibling security/package tools by focusing on community help metrics, though it lacks an explicit verb like 'get' or 'calculate'.
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?
It implies usage when you need Q&A availability metrics for a specific tech tag, and notes the requirement for an API key. No explicit alternatives or exclusion criteria are given, but the tool's unique focus makes the use case clear.
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 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.