ferrolaser-parts

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

No annotations exist, so the description must carry the behavioral burden. It correctly identifies this as a read operation and warns that content is in Chinese, but lacks details on output format, size, or any constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences front-load the purpose and provide key details (topics, language note) without any fluff. 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?

With no output schema, the description should clarify what is returned (e.g., full text, summary). It covers input semantics well but leaves output behavior unspecified.

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%, but the description adds significant value by listing example topic values (e.g., laser-sources, cutting-heads), aiding agent understanding of valid inputs 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 reads a 'category-level overview essay' and lists specific topics (e.g., anatomy of fiber laser machines). This distinguishes it from siblings like get_part_card or list_brands, which have different purposes.

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 use for obtaining an overview essay, but no explicit when-to-use or alternatives are given. The note about Chinese text provides indirect guidance on handling language barriers.

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 provided, so the description must convey all behavior. It states it fetches one card and lists included data, but fails to mention edge cases (e.g., both params provided, no match) or safety (read-only nature). This is adequate but not exhaustive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence with clear subject and details listed concisely. No extraneous information—every word serves a purpose.

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 no output schema and two optional params, the description covers the main use case and response contents. Minor gaps on error handling and parameter precedence, but overall sufficient for a straightforward fetch 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 has 0% coverage, so description must compensate. It adds meaning by specifying card_id comes from search results and title can be exact/partial. However, it doesn't detail format or conflicts when both provided.

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 (Fetch), the resource (ONE knowledge card), and the contents (specs table, wiring, alarm codes, etc.). It distinguishes from siblings like search_parts_knowledge (search) and get_brand_overview (brand overview) by specifying it retrieves a full card by ID or title.

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 after searching (by card_id from search results) and allows direct title lookup. However, it lacks explicit guidance on when not to use or alternatives among siblings.

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 full burden for behavioral disclosure. It does not specify whether the tool is read-only, requires authentication, or any other behavioral traits. The read operation is implied but not explicitly stated.

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, front-loaded sentence with no filler. Every word adds value, making it highly 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?

Given no output schema, the description adequately explains the return content (card counts and one-line intros). For a simple listing tool with no parameters, it is reasonably complete, though it could mention if the list is sorted or filtered.

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?

With 0 parameters and 100% schema coverage, the description adds value by clarifying the output format (card counts and one-line intros) and providing examples of brand categories, which the empty schema does not convey.

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 that the tool lists brands covered by the knowledge base, with card counts and a one-line intro per brand, including examples of brand categories. This distinguishes it from sibling tools like get_brand_overview (which likely gives detailed info on one brand) and 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 usage for obtaining an overview of all brands, but does not explicitly guide when to use this tool versus alternatives like get_brand_overview. No when-not-to-use criteria or prerequisites are mentioned.

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 correctly describes the tool as a search returning excerpts, implying a read-only operation. It does not mention any side effects, permissions, or limitations like pagination, but it provides sufficient transparency for a search tool.

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 sentences with no wasted words. It front-loads the main action (search 521 cards), then offers query and filter guidance, and finally explains output and the next step tool. 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?

The description covers the tool's scope, parameter guidance, and links to a sibling tool. It mentions the default 'top' value but could further explain the ordering of results or behavior with invalid brand. Overall, it is well-rounded for a search tool without 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?

The description adds meaning beyond the bare schema by giving examples of query values (model numbers, error keywords) and enumerating acceptable brand values. It also implies the 'top' parameter controls result count via the default value. This compensates for the 0% schema-description coverage.

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 'Search 521 knowledge cards about fiber laser machine parts' and provides specific examples of models and brands, making the purpose unmistakable. It also distinguishes from sibling get_part_card by noting it returns excerpts.

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 advises querying by model, error keyword, or topic, and lists specific brand options. It also directs users to use get_part_card for full text, providing clear when-to-use guidance. However, it does not explicitly contrast with other siblings like list_brands or submit_inquiry.

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 full burden. It discloses that a human replies within one business day, which is useful. However, it does not mention side effects, data handling, or confirmation of submission, leaving some behavioral gaps for a write operation.

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 sentences, concise, and front-loaded with the core purpose. Every sentence adds value, with no redundancy or 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?

Given the tool's low complexity (no output schema, no nested objects, no enums), the description reasonably covers purpose, basic usage, and response time. However, it could mention post-submission behavior (e.g., confirmation message or ticket ID).

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%, meaning the schema provides no parameter descriptions. The description only explains the three required parameters (name, email, message) but does not address optional parameters (phone, company, country, part_or_model). This leaves significant ambiguity for an agent using the tool.

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 purpose: sending an inquiry to FerroLaser's sales engineers for quotes, part/component sourcing, or technical consultation. It distinguishes from siblings like get_part_card and search_parts_knowledge, which are read-only informational tools.

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 when to use the tool (for inquiries) and lists required fields (name, valid email, message). It implicitly differentiates from sibling tools that retrieve information, but does not explicitly state when not to use it or suggest alternatives.

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