Check whether a runtime, database or framework release is still receiving security fixes, and what to move to. Covers about two dozen products read daily from endoflife.date.
| Name | Required | Description | Default |
|---|---|---|---|
| cycle | No | Release line, e.g. 3.9 or 20. Omit for every cycle. | |
| product | Yes | Product as endoflife.date spells it, e.g. python, nodejs, postgresql. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the transparency burden. It adds context about the data source (endoflife.date), update frequency (read daily), and product scope, but does not disclose return format, failure modes, or explicit read-only behavior. The inferred read-only nature from 'Check' helps, but more behavioral detail would be useful.
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-loads the main purpose, and adds a concise scope note. Every word earns its place, with no repetition of the tool name or unnecessary details.
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?
For a simple check tool with two params and no output schema, the description covers the essential purpose, scope, and data source. It lacks explicit output format details and usage alternatives, but these are somewhat inferable. The description is adequately complete for its complexity.
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% with both 'product' and 'cycle' fully described. The tool description adds no extra parameter meaning beyond the schema. Baseline of 3 applies since the schema already documents parameters adequately.
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 uses the specific verb 'Check' with a clearly defined resource ('runtime, database or framework release') and outcome ('still receiving security fixes, and what to move to'). This distinguishes it from sibling tools by focusing on security-fix status and migration targets, not just generic EOL checks.
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 gives no explicit guidance on when to use this tool versus alternatives. It lacks any 'use this when' or 'for X, use Y' statements. The scope note about covering two dozen products is a limitation, not usage context.
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.