Cueprecise
Server Quality Checklist
Latest release: v0.2.5
- Disambiguation5/5
Each tool targets a clearly distinct resource or action: registration, status, outline, summary, query, excerpt, frames, purge, and two writer/validator tools. Even similar tools like excerpt and frames are separated by retrieval vs extraction, and set_chapter_titles vs set_summary write to different artifacts.
Naming Consistency4/5All tools share the consistent cueprecise_ prefix, but the suffix style mixes verbs (register, purge, query), nouns (status, outline, summary, frames), and verb-object pairs (set_chapter_titles, set_summary). This is readable and mostly predictable, but not a uniform verb_noun naming convention.
Tool Count5/5Ten tools is well-scoped for a video analysis pipeline covering registration, status, content retrieval, summarization, querying, frame extraction, metadata editing, and purging. Each tool earns its place without overlap or bloat.
Completeness4/5The pipeline covers the core lifecycle well: register → status → outline/summary/query/excerpt → set metadata → purge. Minor gaps exist, such as no direct full-transcript retrieval tool and no general artifact listing beyond status, but agents can work around these via excerpt paging and status checks.
Average 3.3/5 across 8 of 10 tools scored. Lowest: 2.5/5.
See the Tool Scores section below for per-tool breakdowns.
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- 101 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description only hints at caching ('reuse current summary') and availability ('immediately usable'). It does not clarify side effects, whether the tool modifies state, or what 'packet' means. The transparency is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence and reasonably brief, though it includes some jargon ('packet', 'local summary') that could be clearer. It is not overly verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lacks essential context: what constitutes a 'full video summary', what the output format is, and what a 'packet' refers to. It is not complete enough for an agent to fully understand the tool's role and limitations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has only one required parameter 'video_id' with no description, and the tool description does not mention it at all. With 0% schema coverage and no compensation, the parameter semantics are not clarified.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a verb ('create or reuse') and resource ('summary') but lacks specificity about what the tool actually returns or does beyond that condition. It mentions 'local summary' and 'packet' without clear definitions, making the purpose somewhat vague.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides a condition ('only when the user requests a full video summary') and references the sibling 'cueprecise_set_summary' for improvements, which gives some guidance. However, it does not explicitly distinguish when to use this tool versus other siblings like 'cueprecise_query' or 'cueprecise_outline'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
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, but it only mentions that Gemini usage is an '추정' (estimation), which hints at approximate data. It does not state whether the operation is read-only, whether it has side effects, how it behaves for unknown video_ids, or what error conditions may arise.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, tightly scoped sentence that front-loads the action (조회한다) and enumerates the four queried data categories in a clean list. There is no redundant or extraneous content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one parameter, no output schema, no annotations), and the description adequately names what data is queried. However, it omits the return format/shape of results, does not define terms like '작업 상태' or '청크 진행도', and provides no context about how the status relates to the video_id lifecycle.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single required parameter video_id has zero documentation in both the schema and the description. Since schema coverage is 0% (below the 50% threshold), the description was required to compensate, but it never explains what video_id represents, how it is used, or what formats are acceptable.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool queries status information (작업 상태, 청크 진행도, 산출물 존재 여부, 로컬 Gemini 사용량 추정) using the verb 조회한다, identifying a specific read operation. However, it does not explicitly distinguish itself from sibling tools by name, though the sibling names (set_chapter_titles, excerpt, frames, purge, register, outline, summary, set_summary) make the status tool's distinct role fairly obvious.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention conditions like 'use this to check progress before other operations' or contrast it with sibling tools. The intended usage context must be inferred entirely from the tool name and sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so the description must disclose behavior. It indicates a read operation ('retrieve') but does not mention error handling, authentication, rate limits, or side effects. It also references 'needs_titles' without clarifying whether it is a parameter or a data condition, adding ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (two short sentences) and front-loads the core purpose. However, the reference to 'needs_titles' without schema context introduces confusion, slightly reducing clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description should explain the return structure, parameter usage, and any prerequisites. It only states the retrieval action and a conditional follow-up, omitting details about max_entries behavior, output format, or the nature of 'needs_titles'. This is incomplete for reliable agent usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is only 50% (max_entries has a default note, video_id has none). The description does not explain video_id or max_entries, and it introduces 'needs_titles' which is not in the schema. This leaves the agent unclear about required parameters and the meaning of the conditional.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb (retrieve) and resource (video outline and timestamp TOC), and implicitly distinguishes from sibling tools by noting that setting titles is a separate tool. It is specific about what it does, though it could be more explicit about the output format or scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit guidance: if needs_titles is present, after reviewing the evidence, create titles and call cueprecise_set_chapter_titles. This tells the agent when to use this tool and what to do next, but it does not explicitly state when not to use it or mention alternatives like excerpt or frames.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It does reveal the return type (evidence span, frame, timestamp) and the no-evidence response. However, it omits important details like whether the operation is read-only, what happens if the video_id is invalid, how results are ordered, pagination limits, or error behavior. For a query tool, this is minimal but not entirely absent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences, front-loaded with the core action ('Queries video content') and then detailing the output and edge case. Every word earns its place; no fluff or repetition. It is optimally sized for the information it conveys.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 parameters, no annotations, and no output schema, the description is incomplete. It explains the return type and no-evidence case but omits crucial context: what constitutes a valid query, how to interpret 'evidence span', whether results are paginated, error handling, and any dependencies on other tools (e.g., video registration). An agent would need to guess many operational details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is only 33% (only 'limit' has a description, and it is trivial: 'default 8'). The description does not explain video_id or query beyond their obvious names, nor does it clarify the format of the query, the expected type of evidence spans, or how 'limit' affects results. The description adds no value over the schema, and with such low coverage, it fails to compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action: 'Queries video content' (영상 내용을 질의한다) and specifies the output: evidence span and frame with timestamp. It also notes the no-evidence behavior. This distinguishes it from sibling tools like cueprecise_excerpt (which likely extracts) and cueprecise_summary (which summarizes), making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus the many siblings. It does not mention alternative tools, prerequisites (e.g., video registration), or scenarios where it should not be used. The only behavioral hint is the no-evidence response, which is about outcome, not selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral disclosure burden. It discloses two useful traits: titles are validated before saving, and boundaries/original text are immutable. However, '검증' (validation) criteria are undefined, and there's no mention of overwrite behavior, failure handling, or side effects, which is a notable gap for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two compact sentences, with the main action front-loaded and the immutability constraint neatly appended. No filler or redundancy; every phrase earns its place. It loses one point because the brevity contributes to the under-specification of validation semantics.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a save operation with no annotations, no output schema, and zero parameter documentation in the schema. The description leaves critical unknowns: what validation rules titles must pass, what happens on invalid input, whether existing titles are replaced, and how fingerprint acts. For a state-changing tool, this is under-specified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides zero descriptions for its parameters (0% coverage), so the description must compensate. It clarifies that the titles array holds host-authored titles for needs_titles, but leaves the purpose of video_id and fingerprint entirely unexplained, and doesn't state how the title object's id relates to existing outline data.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb+resource combination: validating and saving host-authored titles destined for cueprecise_outline's needs_titles. It also adds a meaningful negative constraint ('경계와 원문은 바꿀 수 없다'), distinguishing the tool's scope from one that edits boundaries or original text. It falls short of a 5 only because no sibling differentiation is explicitly made.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the use context: this tool is for the needs_titles field populated by host-authored titles. However, it provides no explicit when-to-use guidance, no exclusion conditions, and no named alternatives, leaving the agent to infer when this tool is appropriate vs. other title-handling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosing destructive behavior. It does state that deletion is explicit and that 'chunks' data can be recreated from original audio, which is useful. However, it does not warn that raw/video/all scopes may cause unrecoverable data loss or describe any additional 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief and well-structured, with the core action stated first followed by a compact scope reference. Every sentence contributes meaningful information without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive tool with no output schema and no annotations, the description is too incomplete. It lacks information about irreversibility, authorization requirements, return values, and the precise meaning of several scope values. An agent could not confidently call this tool with the riskiest scopes based solely on this definition.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains the default scope and partially clarifies 'chunks', but it does not describe 'video_id' at all and leaves 'video', 'raw', and 'all' scopes semantically ambiguous. The enum values are listed in the schema, but the description adds only minimal clarification.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a clear action, '영상 자료를 명시적으로 삭제한다' (explicitly deletes video material), and enumerates the affected scopes. Among the sibling tools, none are deletion-oriented, so this tool is clearly distinguished by its destructive purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus alternatives. It does not mention any sibling tools, prerequisites, or conditions for safe use, only defines what the scopes mean. The regenerability note for chunks implies one usage consideration but does not constitute usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It mentions storing data and server-side decisions on boundaries, but does not disclose whether existing data is overwritten, what the return value is, or potential error conditions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences with no extraneous information, effectively communicating the core behavior and constraints.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lacks essential context: it does not explain what a 'cueprecise_summary packet' is, how video_id and fingerprint relate, or what success/failure looks like. Given the nested schema and no annotations, this is insufficient for confident use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no descriptions for video_id, fingerprint, or content, and the description only references the summary packet without explaining the expected structure or semantics of the content object, leaving parameter meaning largely ambiguous.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool validates and stores a structured summary based on the cueprecise_summary packet, and explicitly notes that chapter boundaries and timestamps are determined by the server, which defines its core purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by referencing the cueprecise_summary packet and server-determined boundaries, but does not explicitly differentiate from sibling tools (e.g., cueprecise_set_chapter_titles) or state when this tool should be preferred over alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations provided, so the description must carry the full burden. It indicates a state-changing action ('registers' and 'runs pipeline') but does not detail side effects such as network calls, storage modifications, or potential destructive actions. It is moderately transparent but lacks explicit side-effect disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, consisting of two short sentences. It front-loads the primary purpose and then adds a note about the optional 'stages' parameter. Every word serves a purpose, with no redundancy or irrelevant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the core action and the key parameter behavior, but it omits information about return values, error handling, or whether the analysis pipeline is synchronous or asynchronous. Given the absence of an output schema, this missing context could leave an agent unsure about what to expect after calling the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides 100% coverage and the description adds meaningful details. For 'stages', it explains the default list and the 'all' option. For 'language', it specifies the format (comma-separated BCP-47) and default behavior. This goes beyond simple labels, though the description doesn't elaborate on the exact meaning of each stage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: registering a YouTube video and running the analysis pipeline. It uses a specific verb ('registers') and resource ('YouTube video'), which distinguishes it from siblings like 'cueprecise_status' or 'cueprecise_excerpt', though it does not explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions that providing 'stages' allows re-running only some steps, which gives a hint about the optional parameter. However, it does not explicitly state when to use this tool versus other sibling tools, nor does it describe any prerequisites or scenarios where this tool is preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
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 side effects. It transparently states that it updates frames.json, fetches low-resolution video under certain conditions, and makes no Gemini call. This gives the agent a clear picture of behavioral consequences, though it does not mention permissions or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, consisting of three short sentences with no fluff or redundant information. It is well-structured: statement of action, conditional fallback, and explicit note about no Gemini call. Every sentence contributes meaningful information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description gives adequate context for an agent to understand the tool's core purpose, side effects, and a notable non-behavior (no Gemini). It lacks some details (e.g., what constitutes 'screen reference times' or output format), but given the simplicity of the tool and the absence of an output schema, it is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes two of three parameters (at and max_frames), and the description adds minimal context by referencing 'screen reference times' which helps interpret 'at'. However, video_id is not described, and the description does not elaborate on parameter ranges or units. The description adds some value but not enough to exceed the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: extracting frames at screen reference times and updating frames.json. It uses specific verbs ('extract', 'update') and identifies the resource (frames.json). It also distinguishes itself by explicitly stating it does not call Gemini, which helps differentiate it from other tools that might use Gemini.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives. It mentions conditional behavior (e.g., fetching low-res video when video is absent or --skip-video is used) but does not state use cases or compare with sibling tools. There is no 'use this when...' or 'instead of...' instruction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the truncation behavior and the need to advance the start value, which is a key behavioral trait. Since no annotations are provided, the description carries the burden, and it does well by revealing this non-obvious behavior, though it does not explicitly state whether the operation is read-only.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is exceptionally concise, using just two sentences to convey purpose, output contents, truncation behavior, and continuation strategy. No superfluous words or redundant explanations are present.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description gives enough context for an agent to understand the basic invocation and output type (subtitles and frames), but it omits details like time units, frame format, and the exact output structure. Given that no output schema is defined, a bit more detail would improve completeness, but it is still adequate for a basic call.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description only indirectly references the 'start' parameter in the truncation note, while 'video_id' and 'end' are not explained at all. With zero schema-level descriptions and no parameter annotations, the description fails to convey the meaning or units of these parameters, leaving the agent to guess.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: retrieving subtitle text and frames for a specified time interval. It uses a specific verb ('조회한다') and resource ('자막 원문과 프레임'), and the truncation note helps distinguish it from related tools like cueprecise_frames or cueprecise_query.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance for handling truncated results by moving the start parameter to continue fetching, which is actionable. However, it does not explicitly compare with sibling tools or state when to choose this over alternatives, so it loses a point.
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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