AutoID Product Catalog & Support

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 what is reported (reachability, declared authoritative commercial fields, relation capabilities), which is useful. However, it doesn't say what happens on failure, whether results are cached, or the response shape. For a health-check tool the disclosure is reasonable but incomplete.

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?

Single sentence that front-loads the core purpose (reachability check) and adds the reporting scope. No waste, though it could be slightly tightened.

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 no-param health-check tool with no annotations and no output schema, the description should ideally explain what a healthy vs unhealthy result looks like and how the reported fields are structured. It covers the purpose but leaves the return semantics entirely unspecified, which is a gap given there is no output schema.

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?

Zero parameters, and schema coverage is 100%, so parameter semantics baseline is 4. There are no parameters to describe, and the description adds no parameter information because none is needed.

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?

States a specific verb (checks) and resource (AutoID canonical API) with the scope of the check (reachability, declared fields, relation capabilities). It distinguishes from sibling autoid_support_health by naming 'canonical API' and 'commercial fields', though it doesn't explicitly say how it differs.

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?

Usage is implied, an agent would infer this is for diagnosing the AutoID canonical API, but there is no explicit when-to-use, no stated alternatives (e.g., vs autoid_support_health), and no prerequisites. Adequate for a simple no-param health check.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 does disclose that the source is 'read-only', which is a genuine behavioral trait, but it says nothing about what a health check returns, what a failure looks like, whether it can time out, or any latency/auth characteristics. For a monitoring tool with no output schema this is thin.

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?

A single front-loaded sentence with no filler. It is appropriately sized, though that brevity comes at the cost of the behavioral detail flagged elsewhere.

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 tool has no annotations, no output schema, and no parameters, so the description must convey everything about behavior. It identifies the target and its read-only nature but omits what the check reports, making it only minimally complete for a health-monitoring tool.

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 tool takes zero parameters, so there is no parameter semantics to explain; the baseline for a parameterless tool applies. Nothing in the description conflicts with the empty 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 names a specific verb ('Checks') and resource ('the read-only AutoID Support Center MCP source') and enumerates the content it covers (manuals, firmware, drivers, software, videos, troubleshooting). It is clearly a health-check operation, though it never explicitly contrasts itself with the sibling autoid_api_health, leaving the differentiation implicit in the word 'MCP source'.

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?

No when-to-use guidance is provided. There is no statement about when an agent should run this check, how often, or under what failure conditions. The existence of a sibling health tool (autoid_api_health) makes the absence of routing guidance a noticeable gap.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does meaningful work: it discloses that returned text is 'verified' and may include warnings, scope, provenance, and version/OS metadata. It does not cover failure behavior for invalid IDs or any rate limits, which is the remaining gap.

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 tight sentences with no filler; the core action and ID format lead, followed by how to use the returned data. The grounding directive is placed where it matters.

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, so the description must describe returns, and it does so thoroughly (verified text, warnings, model scope, official URL, version/OS metadata, source provenance). For a single-parameter fetch tool this is essentially complete.

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% and the single id parameter already documents the exact pattern and examples, so the description's restatement of the model:<id>/resource:<id> form adds little beyond the schema. 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?

States a specific verb (Fetch) and resource (full verified text and metadata for one result returned by search_support), plus the ID namespace it operates on. It is clearly distinguishable from the sibling search_support, which produces the IDs this tool consumes.

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?

Establishes the workflow context: it consumes IDs from search_support and its output is the grounding source for technical answers, with a clear directive against substituting nearby models or inventing versions. No explicit when-not-to-use case, but the sequencing is unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the behavioral burden and does so well: it explains the no-cloning behavior across NEW/REFURBISHED/USED offers, that condition is only projected when source-declared, and that condition is never inferred. It does not cover error behavior or response contents, but the core semantics are clearly disclosed.

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 sentences, front-loaded with the core action, with no filler. Each sentence adds a necessary piece of information: what the tool returns, how conditions work, and when to call the sibling tool.

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 2-parameter read operation with no output schema or annotations, the description covers invocation, condition semantics, and the required companion call. The main gap is the absence of any indication of what 'canonical data' contains, but that is not critical for correct invocation.

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 50%, so the description must add value beyond the schema. It does: 'Use condition to project a source-declared condition when present' gives behavioral meaning to the parameter beyond its enum values, while the sku parameter is already adequately described as an exact SKU.

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?

States a specific verb ('Returns canonical data') and an exact resource ('an exact AutoID SKU'), making the tool's scope clear. It also distinguishes itself from get_product_offer by noting that current commercial data requires a separate call.

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 directs the agent to call get_product_offer alongside this tool for current commercial data, and explains when condition projection applies. It stops short of naming other alternative tools or exclusion cases, but the guidance is unambiguous for the main decision.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does disclose substantive behavioral semantics: group prices are FROM prices rather than exact SKU prices, available_from excludes zero-stock members, and ron_display values are not the commercial authority. It does not cover auth requirements, rate limits, or pagination, so it falls short of a 5.

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 purpose is front-loaded in the first clause, followed by three tight caveat sentences that each carry domain information. Dense with jargon but no sentence is filler; slightly heavy for a one-parameter lookup.

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 or annotations exist, so the description must carry return-value semantics, and it usefully explains the key fields catalog_from, available_from, and pricing.ron_display. It stops short of describing the full grouped-product response shape, leaving some gaps for a tool whose output is otherwise undocumented.

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% and there is a single required 'model' parameter, so the schema already documents it fully. The description's 'such as MC9300' merely echoes the schema's example, adding no new syntax or constraint detail. Baseline 3 applies.

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?

States a specific verb ('Returns') and resource ('authoritative grouped-product information') scoped to an AutoID model like MC9300. The 'grouped-product' framing implicitly distinguishes it from the singular get_product sibling, but no sibling is named explicitly.

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 explains the meaning of returned fields but never says when to choose this tool over get_product, get_product_offer, or list_product_variants. There are no stated prerequisites or exclusions, leaving routing entirely to inference from the name.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden and delivers extensively. It discloses that offers are independent per condition, lists optional fields for refurbished/used, and crucially explains the source_support=false behavior instead of treating a missing condition as unavailable. This goes well beyond a basic 'returns offers' statement.

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 four dense sentences with no filler. The main purpose is front-loaded, and every additional sentence adds unique value: condition structure, field details, usage directive, and error behavior. It is well organized and efficient.

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?

There is no output schema, so the description must convey the return shape, and it does. It enumerates the offer groups a SKU may expose, the additional fields for refurbished/used, and the exact behavior when the canonical API lacks a condition. For a single-SKU lookup tool, nothing critical is missing.

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 only 50%: the 'sku' parameter is documented, but 'condition' lacks a description. The description compensates by explaining the condition dimension (new, refurbished, used) and the edge-case behavior for unsupported conditions. It does not fully map the parameter to its 'all' default or enum mechanics, so it stops short of a 5.

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 states a specific verb and resource: 'Returns the current authoritative commercial offer data for one exact SKU.' It clearly distinguishes itself from sibling product tools by focusing on offers, price, stock, and condition-specific data, making it easy for an agent to select it over get_product or get_product_group.

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 explicit when-to-use guidance: 'ALWAYS use this tool before answering current price, stock, availability or delivery questions.' This is strong usage context, though it does not name sibling alternatives or explicitly state when not to use the tool, so it misses the full 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does so well: it discloses that compatibility evidence comes only from product_tag, that relation type comes from product_cat, the exact default availability semantics (stock_autoid > 0 OR stock_distributie > 0), AutoID-stock prioritization, and that out-of-stock items are omitted by default. Auth requirements and rate limits are unstated, keeping it from a 5.

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?

Front-loads purpose, then layers behavioral detail in dense but information-bearing sentences. Slightly long, but nearly every clause adds a distinct behavioral or filtering fact rather than restating the schema.

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 6-parameter tool with no output schema and no annotations, the description is largely sufficient: it covers filtering semantics, defaults, ordering, and return-card pricing fields (pricing.ron_display, inc-VAT preference). Minor gaps remain around pagination guidance and error/permission behavior.

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 only 50%, and the description compensates by explaining target (model/group SKU or exact product SKU), enumerating relation_type values, and unpacking availability's default and out-of-stock behavior. It also clarifies category as an exact WooCommerce slug with concrete examples. limit/offset semantics are left to 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?

States a specific verb and resource ('Returns first-party related catalog entities for a model or exact SKU') with clear scope and evidence sources. It is distinguishable from get_product/search_products by its related-entity focus, but it never names a sibling to sharpen the boundary (e.g., get_related_products_summary).

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?

Gives strong operational guidance on parameter usage ('request availability=out_of_stock or availability=all explicitly if needed', 'Use category ... to narrow large sets'), but offers no tool-selection guidance relative to alternatives. When to reach for this versus get_related_products_summary or get_product is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the behavioral burden and does well: it discloses that the tool deliberately queries availability=all whereas get_related_products defaults to available, and that it returns a compact summary to avoid loading the full item list into context. It omits permission/auth requirements and any rate-limit or result-size caveats, which keeps it short of a 5.

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?

Four sentences, front-loaded with what the tool returns, then usage, then a behavioral note, then the exclusion. Each sentence contributes distinct routing or behavioral value, though the availability contrast sentence could be tightened slightly.

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 and no annotations, so the description must carry everything; it covers purpose, triggers, exclusions, and the key behavioral divergence from get_related_products. It stops short of describing what the summary's return shape contains (which count fields, how relation types are grouped), which an agent assembling a response might want.

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 67% (target and category documented, relation_type enum undocumented in schema). The description adds meaning for relation_type by referencing 'relation-type counts' and clarifies the implicit availability=all scope, but says nothing about the category slug parameter or how target accepts a model/group SKU versus an exact SKU. Modest value over the schema, so baseline 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?

Names a specific verb and resource: 'Returns a compact catalog summary for related products.' It explicitly positions itself against siblings, contrasting with get_related_products' default availability scope and ruling out get_product_offer for single-SKU commercial detail.

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?

Gives concrete trigger conditions (total compatible product counts, out-of-stock consumable/accessory counts, relation-type counts) and an explicit exclusion (do not substitute for get_product_offer when one exact SKU's commercial details are needed). No alternative routing is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It adds useful context about the taxonomy being canonical and authoritative, which is genuinely valuable information. However, it says nothing about the safety profile, whether this is a read-only operation, the return volume, whether results are cached or paginated, or any potential side effects. For a tool with zero annotation coverage, this is a meaningful gap.

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 with zero waste. The most important information (what it returns and its authoritative nature) is front-loaded. Every sentence earns its place by conveying distinct, useful facts.

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 zero-parameter, read-only list tool with no output schema, the description is reasonably complete in explaining what the tool returns and its authoritative nature. However, without annotations and without an output schema, the description should ideally mention the approximate return structure (e.g., list of brand objects with id/name fields) or confirm it's a read-only operation. It's adequate but has clear gaps regarding what the caller should expect back.

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 tool takes zero parameters, so the baseline of 4 applies. There are no inputs for the description to explain, and the description appropriately does not waste space discussing nonexistent parameters.

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?

States a specific verb (Returns) and a specific resource (the complete canonical WooCommerce brand/manufacturer taxonomy used by AutoID). It clearly distinguishes itself from sibling list tools like list_categories and list_products by naming the exact taxonomy. The agent knows exactly what this returns without opening the schema.

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 implies usage through the scope statement and the note that the taxonomy is authoritative and 'never inferred from product titles,' which hints the agent should use this source rather than deriving brands from product data. However, it does not explicitly state when to call this tool versus alternatives, nor does it mention any prerequisites or exclusions. Usage is implied but not spelled out.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses that it returns the complete taxonomy (no pagination) and includes parent/depth/path data, implying read-only behavior. However, it does not explicitly state read-only status, permissions, 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?

A single sentence that is front-loaded with the core action and resource, with no filler or redundant phrasing. Every word earns its place.

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 the simplicity (no params, no output schema), the description covers the purpose and hints at return content (parent/depth/path). It could be slightly more specific about the exact return format, but it is adequate for an agent to call correctly.

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 tool takes zero parameters, so the baseline score is 4 per the rubric. There is no parameter information to add or compensate for.

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?

States a specific verb ('Returns') and resource ('WooCommerce product_cat taxonomy'), clearly identifying what the tool does. It does not explicitly differentiate from siblings like list_brands or list_product_groups, though the named taxonomy provides implicit distinction.

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 'for category archives and catalog filters' gives implied usage context, but there is no explicit guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description carries the full burden. It discloses the data source (first-party AutoID canonical API) and that clients must page through the entire set, but says nothing about required permissions, rate limits, or what a caller should do on completion. Partial disclosure only.

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 sentences, each front-loading a distinct point: what the tool is, how to call it, and what it is not. No repetition of the title or name and no filler.

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 annotations and no output schema, so the description must carry discovery and pagination semantics, which it largely does. It leaves permissions, response shape, and brand/lifecycle behavior unstated, but covers what an agent needs in order to call the tool correctly.

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 0% and the description compensates by explaining how limit and offset are meant to be used for full enumeration. It does not explain brand or lifecycle semantics, which the schema also leaves undocumented, so it does not fully close the 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?

States a specific verb (enumerates) and resource (canonical grouped-product models) and clarifies it is a discovery tool, not search. It does not name the sibling it contrasts with (search_products), so it stops short of the top 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?

Gives clear when-to-use (exhaustive catalog synchronization via full pagination) and explicitly says not to reconstruct results from search queries. The exclusion is indirect; no sibling tool is named, so it is a strong 4 rather than a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full disclosure burden. It does state the operation is read-only, exhaustive, and paginated ('Pages through'), which is useful. However it omits return shape, whether unpublished products exist, authentication or rate-limit behavior, and how paging interacts with the large offset range.

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 tightly written sentences, front-loaded with the core purpose and scope. Each sentence adds distinct value: scope, contents, and filter guidance. No filler.

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, no annotations, and 0% parameter coverage mean the description should carry more. It is adequate for invoking the tool but leaves the agent without knowledge of the response shape, default page size, or what 'exhaustive' implies for cost across a 500-item-max page.

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 0%, so the description must compensate. It adds real meaning for entity_type by tying 'grouped' and 'single' to the completeness/filtering intent, and 'Pages through' hints at limit/offset paging. But the limit default/max and offset semantics are left entirely to 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?

States a specific verb and resource (list/read the full product registry), plus the exact scope: every published WooCommerce product, including grouped models and standalone/exact products. It explicitly contrasts with searching, so an agent can distinguish it from search_products without opening either schema.

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?

Gives a clear use case (catalog synchronization) and an exclusion: don't approximate completeness from search. It also names the filter values that dispatch the tool. It stops short of explicit when-not conditions or pointing at sibling list tools like list_product_groups.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses important behaviors: condition=refurbished/used only filters source-declared enabled offers, never infers condition from price/text, default availability=available, and AutoID stock is prioritized over distribution-only stock. This goes beyond the schema and gives the agent critical operational knowledge. It doesn't mention pagination or rate limits, but the schema covers limit/offset defaults.

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 compact and front-loaded with the core purpose. Every sentence adds value: the first states what it lists, the second explains the condition contract, the third clarifies filter semantics, the fourth states defaults, and the fifth explains stock prioritization. It's slightly dense but not bloated. Could be improved by separating the stock prioritization into a 'Notes' section, but it's still 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?

For a list tool with 5 params, no output schema, and no annotations, the description covers the key behavioral nuances: condition filtering, availability defaults, and stock prioritization. It doesn't describe the return shape (e.g., whether it returns offers per SKU or just SKU strings), but the tool name and purpose imply SKU listing. The absence of output schema raises the bar, and the description mostly meets it, though a note on response structure would make it complete.

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 only 20% (only 'model' has a description). The description compensates by explaining the semantics of condition and availability filters, including the 'never infers condition' rule and the stock prioritization behavior. It doesn't explicitly explain 'autoid' vs 'supplier' availability values, but the description's context about AutoID stock prioritization helps. This is strong compensation for a low-coverage 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 states a specific verb ('Lists'), a specific resource ('exact SKUs belonging to an AutoID grouped product'), and distinguishes it from siblings like list_products and get_product_offer. It also clarifies the relationship to the canonical API's condition contract, which makes the tool's purpose unambiguous.

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 clear context on when to use this tool (for AutoID grouped products, exact SKUs) and explains filtering behavior for condition and availability. It doesn't explicitly name alternative tools or say 'use X instead', but the sibling list and the specificity of 'AutoID grouped product' imply the right context. A small gap: no explicit exclusion of non-AutoID products or direction to list_products for those.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 does disclose data provenance ('first-party AutoID canonical API'), a result field (pricing.ron_display), and a domain rule (prefer inc-VAT, never convert EUR), which is genuinely useful. However it says nothing about ranking, pagination behavior, or auth/rate limits, leaving real gaps.

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?

Four short sentences, front-loaded with the primary action and usage rule. The pricing/currency guidance is somewhat verbose but is substantive domain instruction rather than filler.

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 output schema and no annotations, the description covers provenance, the key pricing field, and selection guidance, which is enough to call the tool correctly. Return-shape and pagination details remain unstated but are secondary for a search endpoint.

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 50%: query is documented in the schema and the description largely restates it ('by model, SKU or text'), adding little. The limit parameter is undocumented in both places, though its name/default make it largely self-explanatory. Baseline 3 fits a partial-coverage search 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?

States a specific verb and resource ('Search the official AutoID catalog by model, SKU or text') and distinguishes scope from exact-lookup siblings via 'Use this first when the exact SKU is unknown'. An agent can tell it apart from get_product/get_product_group without opening a schema.

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?

'Use this first when the exact SKU is unknown' gives a clear activation condition and implies the alternative (exact SKU lookup), plus 'prefer exact product groups and SKUs over loosely related resources' guides result selection. It stops short of naming the alternate tool explicitly or stating 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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does disclose meaningful behavior: results are read-only first-party AutoID records, the search layer is intent-aware and re-ranks resource-type matches, and agents should not infer firmware versions or procedures from titles. It omits result count, pagination, and any auth or rate-limit context, so it is 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?

Four sentences, front-loaded with the action and scope, then workflow ordering, then a caution. Every sentence contributes something, though the ranking-mechanics sentence is somewhat inside-baseball and could be trimmed.

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 single-parameter search tool with no annotations and no output schema, this covers purpose, workflow position, result provenance, and a reliability caveat. The main residual gap is that nothing describes the shape or volume of returned results, which the description would otherwise need to carry.

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 single 'query' parameter is already documented with examples (MC9300 firmware, ZT610 driver) and length bounds. The description adds only the notion that model-wide and resource-type matches are merged and ranked, which is marginal added meaning.

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?

States a specific verb and resource ('Search the official AutoID Support Center') and enumerates the resource types it covers (model, manual, driver, firmware, software, troubleshooting article, video). The support-resource scope cleanly separates it from sibling search_products, and it names fetch_support as the downstream step.

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?

Gives explicit sequencing guidance: 'Use this before fetch_support and fetch every resource you rely on.' It tells the agent where this fits in the workflow but offers no exclusions or conditions under which a different sibling (e.g., search_products) would be preferred.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.