List or search the products endoflife.ai tracks (480+). Pass an optional "query" substring to find the canonical slug for a product before calling the other tools (e.g. "postgres" → "postgresql"). Returns matching product slugs.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Optional case-insensitive substring filter. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool returns matching product slugs and implicitly indicates this is a read-only operation by using 'List or search.' It does not detail higher-level traits like pagination or error behavior, but for a simple listing tool this is adequate.
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 every sentence earns its place. The example and instruction to use before other tools are relevant and concise.
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 tool with one optional parameter and no output schema, the description fully explains what it does, how to use it, and what it returns (matching product slugs). It also includes enough context (480+ products, canonical slug usage) to integrate with sibling tools.
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?
While the schema already fully describes the 'query' parameter (case-insensitive substring filter), the description adds meaningful context with the example 'postgres' → 'postgresql' and explains the purpose (finding the canonical slug). This enriches the schema's dry definition.
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 or searches products tracked by endoflife.ai, with a specific verb ('List or search') and resource ('products'). It also distinguishes itself from siblings by focusing on product discovery/slug resolution, and mentions the 480+ product count.
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 explicitly recommends using this tool 'before calling the other tools' to find canonical slugs, providing clear usage context. It stops short of naming alternative tools explicitly, but the guidance is unambiguous and sufficient for this read-oriented list tool.
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 clearly distinct purpose: checking a specific version's EOL status, retrieving full lifecycle history, scoring risk, searching products, and batch auditing. No overlap in functionality.
All tool names follow a consistent verb_noun pattern using snake_case (e.g., check_eol, get_product_lifecycle, list_products), making them predictable and easy to distinguish.
With 5 tools covering listing, single-version check, full lifecycle retrieval, risk scoring, and batch scanning, the set is well-scoped for the domain with no unnecessary or missing pieces.
The tool set covers the essential workflows: discovering products, checking individual versions, obtaining full lifecycle data, quantifying risk, and auditing stacks. No obvious gaps for the stated purpose.
