APICK AI

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

Beyond readOnlyHint/openWorldHint, it discloses statelessness, requiring the full conversation history on each call and the reuse of compacted_messages for the next turn. It also flags the point deduction proportional to token usage, adding cost awareness.

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 bilingual and includes several technical details, but every sentence conveys meaningful information: purpose, stateless behavior, model lookup, and cost. It is somewhat longer than necessary due to the duplication, but remains well-structured.

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 mentions receiving an 'assistant reply' and the compacted_messages field, but does not detail the full response structure (e.g., usage metadata). Yet it covers the core workflow and prerequisites adequately for a chat 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?

All 8 parameters are described in the schema with 100% coverage, so the description doesn't need to explain each parameter. It reinforces the critical multi-turn mechanism (compacted_messages) which the schema already documents, adding no new semantic value beyond the 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 opens with a specific, action-oriented statement: 'Send a chat request to a selected LLM model and receive the assistant reply.' It clearly names the resource (LLM model) and the operation (chat request), and differentiates from siblings like llm_models (which lists models) and text_polish/text_summary (which are specialized text tasks).

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?

It explicitly points to llm_models for discovering available models, establishing a prerequisite workflow. However, it does not explicitly state when to prefer this over specialized text tools or provide exclusions, so it offers clear context but no full 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?

Annotations already declare readOnlyHint=true, and the description adds behavioral context beyond that: it notes the response is free, includes all listed fields in one response, and covers text-generation models only. No contradiction with annotations; the description supplements them with cost and response-aggregation details.

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 front-loaded with the key English purpose, but it is somewhat redundant: the Korean translation repeats the English statement, and '무료입니다' appears twice ('무료입니다. [무료]'). It could be tightened without losing meaning, but it is still reasonably compact and structured.

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 compensates by naming the exact fields returned (input/output unit price per 1M tokens, family, size, multimodal flag, tags, use_cases, max_context) and stating they come in a single response. It also explains the tool's relationship to llm_chat. This is adequate for a read-only catalog tool, though it doesn't describe response pagination or exact formatting.

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% with detailed descriptions for all four parameters (tag, family, use_case, multimodal), so the description does not need to compensate. The tool description adds context about output fields but not about parameter semantics beyond what the schema already provides, matching the baseline of 3.

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 and resource: 'List available text-generation LLM models with per-token pricing and max context.' It also differentiates from siblings by stating that the tool is used to find model input values for llm_chat, which distinguishes it from chat, image generation, and text processing 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?

It provides clear context: 'llm_chat Tool의 model 입력값을 찾을 때 사용합니다' (use when finding model input values for llm_chat Tool). This tells the agent when to select this tool, though it does not explicitly state when not to use it or name alternatives as exclusions.

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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the description does not need to repeat safety. It adds valuable behavioral context: fixed model/parameters, optimized for fast response, fixed point deduction regardless of token count, and meaning preservation. This goes beyond the annotations, though it omits output shape and failure behavior. No contradiction.

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 front-loaded with the core purpose in English, followed by a Korean translation that adds minor detail (sentence order) and cost/performance notes. The bilingual repetition is slightly redundant, but the overall length is appropriate and each sentence contributes to understanding. It is well-structured but not maximally concise.

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 simplicity (one parameter, no output schema, strong safety annotations), the description provides sufficient context: purpose, size limit, cost, and performance traits. It does not explicitly state the return value, but that is obvious for a polishing operation. Minor gaps like language support or error cases are not critical for this scope.

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 covers the single 'text' parameter with a description including the 100,000-character limit. The tool description repeats this limit but adds no new semantic detail (e.g., formatting, language, or examples). Since schema coverage is 100%, the baseline of 3 applies, and the description does not elevate it.

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 verb ('Polish'), a resource ('a text'), and the scope (up to 100,000 characters). It also enumerates the types of fixes (grammar, spelling, awkward phrasing), which distinguishes it from sibling tools like text_summary (summarization) and llm_chat (conversation). The title '텍스트 다듬기 AI' reinforces the purpose.

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 through its purpose and constraints (max length, fixed server parameters, fast response, cost), but it does not explicitly state when to prefer this tool over alternatives or provide exclusions (e.g., 'for large texts use X'). No alternative tools are mentioned. The guidance is implicit rather than explicit.

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?

Beyond the annotations (readOnlyHint, openWorldHint), the description discloses that model/parameters are server-fixed, optimized for speed, and that a fixed point cost is deducted per request regardless of token count, including the exact cost of 100 points per call. This adds substantial behavioral context not captured elsewhere.

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 mostly concise and front-loaded, but the first two sentences duplicate the same information in English and Korean. It includes useful cost and model-fixed details. The redundancy is minor, so it earns a 4.

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 tool with one parameter and no output schema, the description covers the main aspects: purpose, input limit, model behavior, speed, and cost. It could explicitly mention the output format or input language, but these are minor gaps, making it largely 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 describes the single 'text' parameter with max 100,000 characters (100% coverage). The description adds no additional meaning beyond restating the character limit and does not provide syntax or formatting details, 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 function: 'Summarize a long text (up to 100,000 characters) into a concise Korean summary.' It uses a specific verb (summarize) and resource (long text), and distinguishes from siblings like text_polish by focusing on summarization rather than polishing.

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 the tool is for summarization but does not explicitly say when to use it versus alternatives like text_polish or llm_chat. It provides context (fixed model, fast response, cost) but no exclusions or alternative references, leaving usage to be inferred.

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