ferrolaser-parts

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

With no annotations, the description carries the full burden. It explicitly uses 'Read' to indicate a non-destructive operation and discloses that the content is in Chinese with translation needed. It lacks details on return format or error behavior, but for a simple read tool this is reasonably transparent.

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 three concise sentences: purpose, topic list, and language note. Every sentence adds value, and the main action is front-loaded.

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?

This is a simple read tool with one parameter and no output schema. The description covers purpose, topics, and translation behavior. It could specify the exact return format, but 'essay' implies text, and the description is mostly complete for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, and the description compensates by enumerating concrete topic values (anatomy, laser-sources, cutting-heads, etc.). This gives clear guidance for the 'topic' parameter, adding meaning far beyond the bare 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 the tool's function: 'Read a category-level overview essay.' It specifies the verb and resource, and the list of topics (anatomy, laser-sources, etc.) distinguishes it from sibling tools like get_part_card or search_parts_knowledge.

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 when to use the tool (for reading overviews on the listed topics) but does not explicitly state alternatives or when not to use it. The note about translation provides a usage hint, but there is no exclusionary guidance or reference to sibling tools.

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 discloses partial title matching and the full-card content, but omits behavior when multiple titles match, when both parameters are provided, or error handling. This is a gap for a tool with no annotation safety net.

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 a single, well-structured sentence that front-loads the action and includes all necessary details without redundancy. 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?

For a simple fetch tool, the description covers purpose, content, and lookup identifiers, which is mostly complete. However, it lacks clarification on parameter requirements and edge-case behavior, which is significant given there are no annotations and the schema lists both parameters as optional.

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 schema has 0% description coverage, so the description must compensate. It does add meaning by explaining that card_id comes from search results and title can be exact or partial, which goes beyond the bare parameter names. However, it doesn't clarify that at least one parameter is required or precedence when both are given, preventing 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 uses a specific verb and resource: 'Fetch ONE knowledge card in full' and lists the included content (specs table, wiring, alarm codes, consumable part numbers, field notes). This clearly distinguishes it from sibling tools like search_parts_knowledge and get_brand_overview.

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?

It indicates when to use the tool by noting 'card_id (from search results)' and specifies the two lookup methods (by ID or exact/partial title). However, it does not explicitly contrast with sibling tools or state when not to use it, so it stops short of a 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 full disclosure responsibility. It does reveal output structure (card counts, one-line intros) and the scope (brands covered by the knowledge base), but it doesn't mention potential limitations such as ordering, filtering, pagination, or behavior if no brands exist. For a simple read-only listing tool, this is adequate but not richly transparent, hence a 3.

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 a single, well-structured sentence that front-loads the action ('List the brands') and immediately provides useful context (card counts, one-line intro, categories). Every word earns its place; there is no redundancy, filling, or unnecessary detail.

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 tool's simplicity (no parameters, no output schema), the description sufficiently covers the key elements: what is returned (brands, counts, intros) and the scope (knowledge base categories). It doesn't specify formatting, sorting, or response shape, but for a list endpoint this is a minor gap. The sibling context and the straightforward nature of the tool make the description reasonably complete, though not exhaustive.

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 has zero parameters, and the schema confirms this with an empty properties object. According to the rubric, 0 params merits a baseline of 4. The description adds no parameter semantics because there are none to explain, so it meets the baseline without penalty.

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's function: 'List the brands covered by this knowledge base with card counts and a one-line intro for each.' It uses a specific verb ('List'), specifies the resource ('brands'), and adds meaningful detail about the output format and categories (laser sources, cutting/welding heads, control systems). This distinguishes it from sibling tools like get_brand_overview (likely focused on a single brand) and search_parts_knowledge (search-oriented).

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 conveys clear context for use: when a user wants an overview of all covered brands, not a specific brand's details. While it doesn't explicitly name alternatives or state when-not-to-use, the sibling tool names and the list-oriented phrasing imply that this is the entry point for brand-level browsing. This meets the 'clear context, no exclusions' bar for a 4.

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?

The description discloses that it returns 'best-matching cards with excerpt', indicating output format and selection behavior. It also states the corpus size (521) and defines query types. However, it does not mention edge cases like empty results or sort order, but given no annotations, it carries the burden well enough.

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 concise yet dense with useful information: purpose, query strategies, brand values, result format, and pointer to alternative. It uses a single, well-structured sentence with clear separators, and every clause 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?

Given the absence of annotations and output schema, the description provides substantial context: corpus size, query types, examples, brand filter, and result behavior. It lacks details on the 'top' parameter and result ordering, but overall it is complete enough for an agent to decide whether to use this 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?

Schema coverage is 0%, so the description compensates. It explains the 'query' parameter with concrete examples ('BLT421', 'FSCUT2000') and the 'brand' parameter with a list of valid values. It does not describe 'top', but this is a minor omission as it is a standard result-count 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?

The description uses a specific verb ('Search') and clearly identifies the resource ('521 knowledge cards about fiber laser machine parts'). It immediately distinguishes itself from siblings by naming the alternative 'get_part_card' for full text, making the purpose unmistakable.

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?

It explicitly gives usage context: query by model, alarm/error keyword, or topic, and mentions an optional brand filter with a specific list of brands. It also instructs to 'use get_part_card for full text', an explicit alternative, effectively saying when not to use this tool.

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 a human engineer replies within one business day, which is useful behavioral context. However, it does not mention what happens after submission (e.g., confirmation, response channel), rate limits, or any other operational details.

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 exactly two sentences with no fluff. It front-loads the action and then adds essential context (human reply, requirements). Every sentence 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?

For a submission tool with no output schema and 7 parameters, the description is adequate but incomplete. It covers the core behavior and required fields but omits optional parameter semantics and any post-submission expectations. Given sibling tools are retrieval-only, this stands out as an actionable tool, yet more detail on optional inputs and outcomes would make it fully 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 0%, so the description must compensate. It explains that name, email, and message are required and that message should describe the need, but it does not provide any guidance for optional fields like phone, company, country, or part_or_model. The purpose of these optional fields remains ambiguous.

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 action ('Send an inquiry'), the target ('FerroLaser's sales engineers'), and the purpose ('quotes, part/component sourcing, or technical consultation'). It distinguishes itself from sibling tools that are focused on retrieving information rather than submitting requests.

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 specifies when to use this tool: for quotes, sourcing, or consultation. It implies that sibling tools are for research/bypassing human contact, but it does not explicitly state when not to use it or name alternatives. The use cases and human response time provide clear context.

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