North korean dictionary(working)
텍스트 본문에서 북한 용어를 자동으로 추출하고 그 뜻을 사전에서 매칭하여 반환합니다.
Args:
text: 북한 용어를 식별할 본문 원문 텍스트
field: 필터링할 용어 분야 (옵션, 예: '음식', '생활' 등)
csv_path: 사전 csv 파일 경로 (기본값: 북한용어사전.csv)
Returns:
{용어: 설명} 형식의 파이썬 딕셔너리
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| field | No | ||
| csv_path | No | 북한용어사전.csv |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description discloses that the tool extracts and matches terms, returning a dictionary. No annotations exist, so description carries full burden. It is adequate but lacks details on side effects, permissions, or performance considerations.
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?
Description is concise with one paragraph and explicit Args/Returns structure. Front-loaded with purpose, but reads like documentation rather than natural language.
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?
Describes inputs and output format, but does not cover error cases, handling of no matches, or limitations. Adequate for a straightforward extraction tool given lack of annotations and 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 coverage is 0%, but description explains each parameter in Args section: text (original text), field (filtering field with example), csv_path (file path with default). Adds meaning beyond 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 extracts North Korean terms from text and returns definitions. Purpose is specific verb+resource, but does not explicitly differentiate from sibling tool 'search_term'.
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 or when not to use. Sibling tool name suggests an alternative usage but description does not differentiate or offer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
특정 어휘를 지정하여 북한 용어사전에서 개별적으로 검색합니다.
Args:
query: 검색할 북한 용어 (부분 일치 지원)
csv_path: 사전 csv 파일 경로
Returns:
{용어: 설명} 형식의 파이썬 딕셔너리
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| csv_path | No | 북한용어사전.csv |
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 mentions partial match support and return format (dictionary), but lacks details on performance, error handling, or limits. Adequate but not comprehensive.
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?
Description is concise with clear sections for purpose, args, and returns. No superfluous text, front-loaded with 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?
Simple tool with few parameters. Return format is specified, but no output schema exists. No discussion of errors or edge cases. Sibling tool name suggests alternative but no comparison. Adequate for basic search.
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 description compensates: 'query' described as search term with partial match, 'csv_path' as file path with default noted. Adds meaning beyond schema, but not extensive.
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 verb 'search' and resource 'North Korean glossary', specifying individual search. However, it does not differentiate from sibling tool 'extract_and_define', which may have a different search scope.
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 explains what the tool does but provides no guidance on when to use it versus the sibling tool 'extract_and_define'. No exclusions or alternative contexts are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Add one secure layer between your agents and this server.
The two tools have clearly distinct purposes: one extracts and defines terms from a text, the other searches for a specific term individually. There is no overlap in functionality, so an agent can easily select the appropriate tool.
Both names use snake_case, but the patterns differ: 'extract_and_define' combines two verbs, while 'search_term' is verb_noun. This inconsistency, though minor, makes the naming pattern less predictable.
With only 2 tools, the server feels minimal for a dictionary service. While both tools serve core lookup and extraction needs, the count is borderline and could benefit from additional tools like listing all terms or managing entries.
The tool surface covers extraction and individual lookup, but lacks common operations like listing all terms, adding or updating entries, or bulk retrieval. This incomplete coverage may cause agent failures when full dictionary management is needed.