EOL dates and risk scores for 480+ software products. Check versions, score risk, audit stacks.
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Average 4.3/5 across 5 of 5 tools scored.
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.
check_eolAInspectCheck whether a specific version of a software product is end-of-life (EOL). Returns lifecycle status, the EOL date, days remaining or days past EOL, and the latest release. Use this for "is X version Y still supported?" questions.
| Name | Required | Description | Default |
|---|---|---|---|
| product | Yes | Product slug or name, e.g. "postgresql", "nodejs", "ubuntu". | |
| version | Yes | Version/cycle, e.g. "14", "18", "20.04". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It specifies return fields (lifecycle status, EOL date, days, latest release) but does not disclose potential limitations like data freshness or scope of products supported. Adequate but not exhaustive.
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, front-loaded with purpose and return data, followed by usage guidance. No wasted words; every sentence adds value.
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 2 simple parameters and no output schema described, the description sufficiently explains the tool's behavior and return values. Covers the use case completely.
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 coverage is 100%, with descriptions for both parameters. Description adds example values (e.g., 'postgresql', '14') but does not significantly enhance meaning beyond the schema.
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 it checks whether a version is EOL and lists returned data. The verb 'check' and resource 'version of a software product' are specific, and it distinguishes from sibling tools like list_products and scan_stack.
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?
Provides explicit when-to-use context: 'Use this for "is X version Y still supported?" questions.' No exclusions or alternatives mentioned, but guidance is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_product_lifecycleAInspectGet the full version history for one product: every tracked version/cycle with its release date, EOL date, support status, and EOL Risk Score™. Use for "give me the whole EOL schedule for X".
| Name | Required | Description | Default |
|---|---|---|---|
| product | Yes | Product slug or name, e.g. "postgresql". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It describes the output in detail (version/cycle, dates, status, risk score), which informs the agent about the return data. However, it does not mention potential limitations like permissions or rate limits, but for a simple read 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?
The description consists of two short sentences with the main action front-loaded. Every part is useful: the first sentence defines the tool, the second provides a usage context. No unnecessary words.
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?
Given there is no output schema, the description adequately describes the return fields (version, dates, status, risk score). The tool is simple with one parameter, so the description is complete enough for an agent to understand what it does and what it returns.
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%, so the schema already documents the 'product' parameter as a slug or name. The description does not add any additional semantic information about the parameter beyond what is in the schema, so baseline 3 is appropriate.
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 it gets the full version history for a single product, listing specific fields (release date, EOL date, support status, EOL Risk Score™). It distinguishes from sibling tools like check_eol (likely single check) and get_risk_score (single score) by focusing on the complete schedule.
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 includes a direct usage example ("give me the whole EOL schedule for X") which tells the agent when to use it. It does not explicitly state when not to use or mention alternatives, but the context of siblings provides implicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_risk_scoreAInspectGet the proprietary EOL Risk Score™ (0–100) for a product version, with the four-factor breakdown (EOL recency, attack surface, CISA KEV exposure, extended support). Omit "version" to score the product's highest-risk (most recently end-of-lifed) release. Use this to quantify how dangerous it is to keep running something.
| Name | Required | Description | Default |
|---|---|---|---|
| product | Yes | Product slug or name, e.g. "openssl", "python". | |
| version | No | Optional version/cycle. Omit for the highest-risk release. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses the risk score range, four factors, and the behavior when version is omitted (highest-risk release). Without annotations, the description provides sufficient behavioral context, though it does not mention return format or side effects.
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?
Three concise sentences with no wasted words; front-loaded with the core purpose and key 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?
The description is complete for a simple tool with two parameters, covering purpose, behavior, and usage. Lacks explicit output format, but can be inferred from the score range description.
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 coverage is 100% with descriptions, but the tool description adds value by explaining the effect of omitting version and giving an example for product, exceeding the baseline of 3.
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 gets a proprietary risk score (0–100) with a four-factor breakdown, distinguishing it from sibling tools like check_eol and get_product_lifecycle which likely provide simpler EOL status.
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?
Explicitly advises omitting 'version' to score the highest-risk release and recommends using the tool to quantify danger of keeping a product running, but does not explicitly compare to siblings or state when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_productsAInspectList 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.
scan_stackAInspectAudit a whole stack at once. Provide a list of products (optionally with versions) — e.g. parsed from a package.json, Dockerfile, or SBOM — and get an EOL Risk Score for each, so you can see what is unsupported and dangerous in one call. Free tier: up to 5 items; Pro: up to 50.
| Name | Required | Description | Default |
|---|---|---|---|
| items | Yes | List of stack components to score. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, so description carries burden. Discloses tier limits and optional version behavior, but omits error handling, partial results, or authentication needs. Adequate but not 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?
Two concise, front-loaded sentences. First states core action, second provides examples and constraints. No redundancy.
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?
One required parameter with nested structure, no output schema. Description explains return (EOL Risk Score per item). Sufficient for straightforward batch tool, but could detail output format or errors.
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 coverage 100% with basic descriptions. Description adds context on batch usage and tier limits, enhancing understanding beyond schema alone.
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?
Clearly states verb 'audit' and resource 'whole stack at once', providing EOL Risk Scores per component. Differentiates from sibling single-product tools like check_eol and get_risk_score.
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?
Explicitly mentions when to use (batch audit from package.json, Dockerfile, SBOM) and tier limits. Lacks explicit 'when not to use', but context implies single products go elsewhere.
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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