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

With no annotations, the description fully describes the tool's behavior: no topic returns a list of topics with counts; with topic returns related entities. It sets correct expectations for a read-only exploration tool. No hidden side effects are mentioned, but the behavior is transparent enough for use.

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 with two sentences: the first states the action and two behaviors, the second provides the intended use case. Every word is purposeful, and the structure is efficient and clear.

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 moderate complexity (two modes) and no output schema, the description completely covers what the tool does and what it returns in each scenario. It differentiates from siblings and provides sufficient context for an exploration 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 description and parameter description together fully explain the optional topic parameter. The description adds significant value by detailing the two distinct behaviors triggered by its presence or absence, going well beyond the schema's minimum.

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: exploring the environmental topic map. It distinguishes between two modes (without topic for overview, with topic for detailed related items) and explicitly mentions the use case for teachers to browse or expand lesson ideas, which differentiates it from sibling search 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 provides clear guidance on when to use the tool: for browsing the topic map and expanding lesson ideas. It implicitly contrasts with search tools by emphasizing exploration. More explicit differentiation from siblings like search_resources would improve it, but the context is already strong.

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 the chunked reading behavior, default and maximum chunk count, and pagination method. However, it does not describe the output format, error handling, or authentication requirements.

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, no fluff. The first sentence states the purpose, the second gives usage instructions. Efficient and 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?

No output schema exists, so the description should cover return values. It mentions 'metadata and body text' but lacks detail on structure or formatting. Pagination is well explained, but completeness is partial.

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% with descriptions for all parameters. The description adds the context that document_id comes from search_fulltext and confirms default/max values for count, but this largely repeats schema information. 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 it reads the metadata and body text of the original document in continuous chunks, which is a specific verb and resource. It distinguishes itself from sibling tools like search_fulltext by specifying it uses the documentId returned by that tool.

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 explicitly says to call with documentId from search_fulltext and explains how to paginate via from_chunk. It provides clear context but does not explicitly mention when not to use this tool or list alternatives.

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 mentions what is included (achievement standards, curriculum comparison), but does not disclose read-only nature, performance implications, required permissions, or return format. The description lacks important behavioral context for a detail retrieval 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 extremely concise at two sentences with no redundant words. It front-loads the primary action ('조회합니다') and efficiently adds key details about included content.

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 tool with no output schema, the description gives a reasonable overview of what is included (achievement standards, curriculum comparison). However, it does not describe return structure, pagination, or error handling, leaving some gaps given the lack of 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?

Schema description coverage for the single parameter 'id' is 100%, with a clear description. The tool description reinforces the parameter's role but does not add new semantic information beyond what the schema already provides. 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 retrieves detailed information by resource id, and specifies it includes the full content of linked achievement standards and comparisons with the 2022 revised curriculum DB. The verb '조회합니다' and resource '자료 상세' are specific, and the tool is well differentiated from sibling tools like search_resources which returns a list.

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 explicit guidance on when to use this tool versus alternatives. While sibling tools are listed, the description does not state prerequisites (e.g., needing an id from search_resources) or when not to use this tool. Usage context 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?

No annotations provided. Description mentions it returns a list but does not disclose potential pagination, permissions, or error behavior. Adequate but not detailed.

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, each serving a purpose: function and usage scenario. No fluff.

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. Description says it returns a list but does not specify fields. Adequate for a simple lookup but could be more 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% with descriptions for both parameters. The tool description does not add additional semantic value 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 maps an achievement standard code to a list of related materials. It distinguishes from siblings like search_resources and search_standards by focusing on direct lookup by code.

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 explicitly says when to use: after confirming the standard in class to find immediately usable materials. Missing explicit when-not-to-use or alternatives, but the context is clear.

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 cover behavioral aspects. It states that recommendations come with rationale (shared standards, topics, subjects), which is helpful. However, it does not explicitly state that the tool is read-only (non-destructive) or discuss any side effects, permissions, or rate limits. The verb 'recommend' implies safety, but more explicit transparency would improve the score.

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 short sentences that are front-loaded with the main action and immediately followed by a usage scenario. Every word is purposeful, and there is no redundancy. It balances brevity with completeness, earning a top score.

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 moderate complexity (3 optional parameters, no output schema, no annotations), the description covers key aspects: inputs, output content (similar resources with rationale), and usage context. It does not describe return format details like pagination or sorting, but the absence of an output schema lowers expectations. The rationale mention compensates partially.

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 has 100% description coverage for all three parameters, so the baseline is 3. The description adds little beyond the schema: it mentions that resource_id and document_id serve as criteria, but the schema already describes them. The use case context ('비슷한 것 더') provides some semantic enrichment, but not enough to raise the score above baseline.

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 (recommending similar resources) and specifies the input types (resource_id or document_id). It also includes a concrete use case ('when a teacher wants 'more like this'), which distinguishes it from generic search tools. The verb '추천' (recommend) is specific and indicates a non-search operation.

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 explicitly tells when to use the tool ('when a teacher likes a resource and wants similar ones'), providing clear context. However, it does not mention when not to use it or explicitly compare it to sibling tools like search_resources or explore_topics. The guidance is clear but could be strengthened with exclusion criteria.

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 description carries full burden. It discloses that the tool searches over 1053 PDFs (~22k chunks) and that the first call takes 1-2 seconds. However, it does not describe result ranking, pagination, or rate limits.

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 concise sentences: purpose/scope, usage guidance, and fallback/performance note. No redundant information; every sentence 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?

For a search tool with 5 parameters, no output schema, and no annotations, the description covers purpose, usage, a behavioral note, and sibling connection. Lacks output format or pagination details, but adequate for the task.

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 baseline is 3. The description adds context that the query is for full-text content and mentions excerpt truncation, but does not elaborate on each parameter beyond schema. Acceptable but not exceptional.

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 searches full text of 1053 PDFs (environmental education materials) with specific examples of use cases. It distinguishes itself from the sibling tool search_resources (metadata search) and get_document_text (full context reading).

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?

Provides explicit when-to-use guidance: for finding specific activity methods, experimental procedures, concept explanations, and cases that metadata search cannot find. Also advises using get_document_text for truncated excerpts. Notes first call is slow due to index building.

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; description implies read-only search behavior and mentions that results include 'standardCodes'. Basic transparency is adequate, but lacks details on permissions or performance.

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: first states purpose and scope, second explains filtering and points to related tool. No wasted words, 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?

Given 8 optional parameters and no output schema, description provides total count, filter options, and result content hint ('standardCodes'). Could mention result structure but still reasonably 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?

Input schema covers 100% of parameters with descriptions. Description summarizes filter categories without adding new 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?

Description clearly states the tool searches environmental education materials from a specific resource (교육부 자원맵, 933건) and differentiates from siblings like 'get_resource_detail' and 'search_standards'.

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?

Description lists available filters and explicitly suggests using 'get_resource_detail' for detailed content, providing clear guidance on when to switch 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 full burden. It discloses the dataset size and available search dimensions but omits any behavioral traits like pagination, response format, or performance characteristics. This is adequate but not thorough.

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 no wasted words, but the second sentence could be seen as slightly verbose. The key information is front-loaded. Minor redundancy in listing parameters already in 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?

Given 6 parameters, no output schema, and no annotations, the description lacks details on return format, pagination, or behavior of the limit parameter. The example usage is helpful but incomplete. Adequate but missing some context an agent might need.

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 baseline is 3. The description only lists the searchable fields (keyword, school level, subject, grade, code) without adding meaning beyond the already detailed schema descriptions. No significant value added.

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 searches achievement standards from the 2022 revised curriculum, specifying scope (3285 items, all subjects and levels). It distinguishes from sibling tools like search_resources or search_fulltext by focusing solely on standards.

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 a specific use case ('when designing environment classes') but does not explicitly state when not to use this tool or compare with siblings. The context is clear enough for an agent to infer appropriate usage.

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