analyze_code_submission_boj

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

With no annotations, the description carries full behavioral burden. It discloses that the tool combines problem text and user code to generate an LLM analysis prompt, and it specifies the response structure (problemInfo, codeMetadata, analysisPrompts, suggestedQuestions). It does not mention side effects, auth requirements, or rate limits, but the LLM-prompt nature is 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 compact and well-structured, with short sections for analysis types, response structure, usage scenarios, supported languages, and a platform-verification warning. It front-loads the core purpose and every sentence contributes meaningful guidance, with no redundant 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?

The description fully compensates for the lack of an output schema by listing the response fields. It also covers analysis types, languages, and the critical platform-ambiguity rule. For a tool with 4 parameters, 2 enums, and no output schema, this description is remarkably complete for an agent to invoke correctly.

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 covers all parameters with descriptions (100% coverage), so the baseline is 3. The description adds value by explaining each analysis_type in detail (full, hint, debug, review) and listing supported languages, which goes beyond the schema's enum labels. It does not add syntax details for problem_id or code, but those are straightforward.

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 states a specific action: '백준(BOJ) 문제에 대한 사용자 코드를 분석하여 피드백을 제공합니다' (analyze user code for BOJ problems and provide feedback). It clearly distinguishes from sibling tools by naming BOJ and including a platform-verification note that separates it from the Programmers variant.

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 '사용 시나리오' section listing concrete use cases (code improvement, debugging, code review) and explicitly instructs the agent to confirm the platform with the user if only a problem number is given. It does not name alternative tools or explicitly state when not to use it, which prevents a 5.

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