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

With no annotations, the description carries the full burden and does well: it explicitly says the tool provides a guide, including parameter concatenation order, code examples in multiple languages, and precautions. This clearly communicates the informational nature without 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?

The description is two tight sentences: the first states the core purpose, the second enumerates the guide's contents. No wasted words, and the most important information 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?

For a tool with only two parameters and no output schema, the description adequately covers what it does and what it provides. It could mention the exact output format, but the informational nature is clear and sufficient for an agent to use it.

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%, so baseline is 3. The description adds value by connecting api_name to the per-API parameter concatenation order and language to the code example languages, enriching the meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: guiding CheckValue (HMAC SHA256) generation for the Samsung Checkout DPI API. It specifies the resource and differentiates from sibling tools by focusing on this specific hash-generation guide.

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 provides clear context: use this tool when you need to generate a CheckValue for the Samsung Checkout DPI API. It does not explicitly exclude alternatives, but the intended use case is evident.

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 of behavioral transparency. It merely states that the tool provides snippets, without disclosing the output format, whether it is a static read operation, or any side effects. This is insufficient for an agent to fully understand the tool's behavior.

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, concise sentence that immediately communicates the tool's purpose. It is front-loaded and contains no unnecessary words, 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 the simplicity of the tool (one well-documented parameter, no output schema), the description is adequate for understanding the core functionality. However, it could benefit from noting that it returns static code examples or how the snippet is delivered, but this is not a major gap.

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 covers 100% of parameter semantics, including detailed descriptions for each enum value in the parameter description. The tool description adds minimal meaning beyond the schema, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: providing code snippets and boilerplate for Samsung Checkout implementation. It uses a specific verb ('provide') and resource ('code snippets and boilerplate'), which distinguishes it from sibling tools like lookup_api or get_implementation_guide.

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 usage is implied rather than explicitly stated. The description does not mention when to choose this tool over alternatives, nor does it provide exclusions. The parameter enum suggests different snippet types, but the description lacks direct guidance on when to use it versus 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?

With no annotations provided, the description carries the full burden. It partially discloses content ('purchase→verification→application flow and precautions') but does not describe the output format, whether it returns a step-by-step guide, code examples, or other behavioral details. For a read-only documentation tool, this is adequate but not rich.

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

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

The description is two concise sentences. The first sentence clearly states the tool's purpose, and the second enumerates the content covered. No filler or redundant information; the structure front-loads the main action.

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 a single parameter, no output schema, and the description adequately covers the core purpose and content. It mentions the flow stages and precautions, but does not explain the 'all' enum option or clarify whether the guide includes code snippets, which would make it more complete. Still, for a simple documentation tool, this is sufficient.

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 covers 100% of parameter semantics, including an enum with a description. The tool description merely repeats the enum values ('Consumable, Non-Consumable, Limited Period, Subscription') without adding new meaning or clarifying the 'all' option. Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: guiding the overall implementation flow for each Samsung Checkout product type. It explicitly lists the product types (Consumable, Non-Consumable, Limited Period, Subscription) and the flow stages (purchase, verification, application), which differentiates it from sibling tools like get_code_snippet or lookup_api.

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 this tool (for implementation flow guidance per product type) but does not explicitly mention alternatives or exclusions. It does not say 'use this instead of X' or provide any when-not-to-use guidance, leaving usage context slightly underdeveloped.

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 of disclosing behavior. It does not state that this is a read-only informational tool, mention any prerequisites, or describe what the returned checklist looks like. The description only restates the tool's purpose without revealing side effects or limitations.

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

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

The description is two sentences long, directly states the tool's function, and includes a brief scope of content (initial setup to launch). Every sentence contributes meaningful information with no superfluous 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?

For a simple single-parameter tool with no output schema, the description gives enough context to understand its purpose and scope. It could be more complete by explaining the structure of the checklist or any prerequisites, but given the tool's simplicity, it is largely sufficient.

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 input schema fully describes the single parameter 'stage' with an enum of all possible values, achieving 100% schema coverage. The description adds no extra meaning about the parameter beyond what the schema already provides, so the baseline score of 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?

The description clearly states the tool provides a step-by-step guide and checklist for Samsung Checkout development environment setup, from initial setup to testing and launch. This distinguishes it from siblings like lookup_api or troubleshoot, though it does not explicitly contrast with get_implementation_guide.

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 anyone setting up the Samsung Checkout development environment, but it provides no explicit guidance on when to choose this over the sibling get_implementation_guide or other tools. No exclusions or alternative recommendations are given.

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 must fully disclose behavior. It clearly states the tool retrieves and provides API reference content, but it does not explain edge cases, response format, or any limitations. For a read-only lookup, this is adequate but not rich.

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

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

The description is two sentences, front-loaded with the main verb, and includes only relevant details about scope and deliverables. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter lookup tool with no output schema, the description sufficiently covers what the tool does and what it returns. Slightly more detail on response format could enhance completeness, but it is not necessary.

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 api_name parameter includes descriptive enum values. The description adds context by grouping APIs into client and server categories and explaining the purpose of each server API, but most of this is also present in 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?

The description clearly states a specific action ('조회합니다') and resource ('Samsung Checkout API 레퍼런스'), enumerates the covered APIs, and outlines the deliverable (parameters, usage, code examples). This differentiates it from sibling tools like get_code_snippet or get_implementation_guide, which target specific guides rather than full reference lookup.

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 when an agent needs Samsung Checkout API reference material, but it does not explicitly state when to prefer this over siblings like get_code_snippet or search_knowledge. The overlap in 'code examples' with get_code_snippet creates ambiguity, so guidance is only implied.

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 key behavior: keyword search, coverage of all MD documents, and return of original section text. It does not mention auth, rate limits, or result limits, but for a simple read-oriented search tool the core behavior is transparent and accurate.

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

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

The description is two concise sentences: the first states the action and scope, the second states the return type. There is no waste, repetition, or irrelevant 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?

For a simple search tool with one fully documented parameter and no output schema, the description adequately explains the search scope and return value (original text of relevant sections). It could add details like result ordering or count, but it is sufficiently complete for the tool's apparent 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?

The single parameter 'query' is fully documented in the schema with examples, giving 100% schema coverage. The description adds no additional parameter semantics, but because the schema handles the parameter description well, the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb ('검색합니다' = searches) and names the exact resource (Samsung Checkout knowledge base, all MD documents). It also states what is returned (original text of relevant sections), clearly differentiating it from sibling tools like lookup_api or guide generators.

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 clearly defines the scope: searching the entire knowledge base across all MD documents, which implies a broad cross-document keyword search. However, it does not explicitly mention alternatives or when not to use this tool, so it does not fully meet the 5-level for explicit when/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 provided, the description carries the full burden of behavioral disclosure. It only states that it provides causes and solutions without revealing limitations, response format, or whether it relies on external services. It does not disclose any side effects, error handling, or data scope, leaving significant behavioral aspects unknown.

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, containing only two sentences. The first sentence front-loads the main purpose, and the second explains input and output. No unnecessary information or repetition exists, making it well-structured 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?

For a tool with only one parameter and no output schema, the description adequately conveys the core functionality and expected input. It explains what happens (provides causes and solutions) and includes examples. While it lacks details on output structure or limitations, the simplicity of the tool keeps it mostly 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?

The input schema already provides a 100% description coverage for the single 'query' parameter, including examples. The description adds no new parameter information but reinforces the expected input types (error code, message, symptom). Since schema coverage is high, a baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool diagnoses and resolves errors during Samsung Checkout development, with a specific verb ('진단하고 해결 방법을 안내합니다') and resource (Samsung Checkout errors). It distinguishes from sibling tools by focusing on troubleshooting rather than API lookup, guide generation, or code snippets, making its purpose immediately clear.

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 by specifying inputs (error codes, messages, symptoms) and output (causes and solutions), giving clear context for the tool's use. However, it does not explicitly mention when not to use it or compare with alternatives like lookup_api or search_knowledge, so it falls short of a 5.

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