AutoID

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?

No annotations are provided, so the description carries the full behavioral burden. It adds real payload context (WooCommerce RON display values are separate from authoritative EUR metadata), but is silent on read-only semantics, behavior for an unknown/nonexistent SKU, and whether the SKU match is strict. Read intent is only implied by 'Returns'.

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 identity/scope statement followed by the routing instruction and a payload note; no filler. Slightly dense but every sentence carries distinct information.

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 usefully previews the payload (canonical identity data with separate RON display vs EUR metadata) and routes to get_product_offer for volatile fields. Remaining gap is error/not-found behavior for an invalid SKU, minor for a single-parameter lookup.

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?

Only one parameter and schema description coverage is 100%, so the schema already documents it fully and the baseline is 3. The description's 'exact AutoID SKU' merely restates the schema's 'Exact AutoID product SKU' without adding matching rules, format, or casing guidance.

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 canonical data for an exact AutoID SKU') and explicitly contrasts scope with the sibling get_product_offer, which owns price/stock/delivery. An agent can distinguish identity lookups from offer lookups 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 explicit when-to-use ('product identity and stable product data') and names the complementary tool plus its trigger condition ('For current price, stock or delivery availability, always call get_product_offer as well'). Nothing about tool selection 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 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?

No annotations are provided, so the description carries the full behavioral burden. It discloses authoritative vs. display data, surface-specific fields for Romanian output, and disallowed inference paths. It does not state auth, rate limits, or error behavior for unknown SKUs, so it stops 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?

Front-loads the imperative and then layers authority rules. It is dense but every sentence carries operative information about which fields are authoritative and which are display-only. Slightly long, but no obvious 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?

For a no-annotation, single-param tool with no output schema, the description pre-emptively documents return-field semantics (pret_lista, pret_autoid_euro, stock_autoid, stock_distributie, price.ron_display) and commercial authority rules. Nothing an agent needs in order to call it correctly or interpret it 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 100%, and the description adds only the word 'exact' to qualify the SKU parameter. That is consistent with the schema's 'Exact AutoID product SKU' but adds little beyond it. Baseline 3 applies when the schema does the heavy lifting on a single documented parameter.

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 the current authoritative AutoID offer for an exact SKU') and distinguishes itself from siblings like get_product by scope (single exact SKU, live commercial authority). The agent can tell it apart 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?

Explicit imperative 'ALWAYS use this tool before answering current price, stock, availability or delivery questions' tells the agent exactly when to invoke it. It also names the authority rules and what not to do ('Do not convert EUR yourself and do not infer availability from cached descriptions'), which functions as clear when-not guidance.

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?

With no annotations, the description carries the full behavioral burden and does meaningful work: it discloses the default availability filter, the stock-ordering priority (AutoID stock before distribution-only stock), and the shape of returned commercial cards including pricing fields. It omits pagination behavior and truncation semantics for limit/offset.

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 compact sentences with the core purpose and default filter front-loaded, then the availability rule, then return details. Every sentence conveys information; there is no filler or restatement of the title.

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 read-only list tool with no output schema, the description supplies the default scoping, ordering behavior, and the shape of returned card pricing, which is the right level of detail. Minor gaps remain around pagination and the non-default availability enum values, but an agent can invoke it correctly from this.

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 25% (only 'model' is documented), so the description must compensate. It adds useful semantics for availability (default value and the specific condition for 'all'), but says nothing about limit, offset, or the meaning of the autoid/supplier/out_of_stock enum values, leaving half the parameters unexplained.

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 ('Lists exact SKUs belonging to an AutoID grouped product'), which is clearly distinct from generic product search or group-fetch siblings. It scopes the operation to AutoID grouped products, though it does not explicitly name the sibling tool (get_product_group) an agent might confuse it with.

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 an explicit when-to-use rule for the availability parameter: use availability=all 'only when the user explicitly needs unavailable configurations too,' and states the default is available. No alternative tools are named and no prerequisites are stated, so it falls short of full routing guidance.

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.