subtitle-analyzer
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation2/5
The extract_subtitles tool has a 'get_video_info' parameter that overlaps with the separate get_video_info tool, causing ambiguity. Additionally, extract_subtitles and search_timestamp both involve subtitle content but with different purposes, though this is less problematic.
Naming Consistency5/5All tool names use consistent snake_case and follow a verb_noun pattern (extract_subtitles, get_video_info, list_available_subtitles, search_timestamp), making them predictable.
Tool Count5/5With 4 tools covering extraction, listing, info retrieval, and searching, the count is appropriate for the server's subtitle analysis purpose, neither too many nor too few.
Completeness4/5The set covers core subtitle analysis operations, but missing features like subtitle translation or editing are minor gaps given the 'analyzer' focus. The inclusion of format options and search is good.
Average 4/5 across 4 of 4 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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
- Behavior3/5
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 authentication behavior (priority order) but does not mention other traits like read-only nature, idempotency, 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.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two paragraphs: purpose and auth priority. It is front-loaded but could be slightly more streamlined.
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?
With no output schema, the description lists returned fields (title, duration, description). It covers both parameters and provides sibling context. Missing error handling details.
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?
Schema description coverage is 100%, so baseline is 3. The description repeats parameter info and adds authentication priority context for cookies_file, but adds little beyond the schema.
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 retrieves basic video information (title, duration, description). It distinguishes from sibling tools focused on subtitles and timestamp search.
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 authentication configuration priority, guiding the agent on when to use each method. However, it lacks explicit when-not-to-use or comparisons with 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?
With no annotations provided, the description discloses authentication behavior and required parameters. It does not mention other behavioral traits such as read-only nature, error handling, or rate 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured, with a clear purpose statement followed by authentication configurations and parameter details. No unnecessary information is 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?
Given the tool's simplicity (2 parameters, no output schema), the description covers the essential aspects: purpose, authentication, and parameter details. It lacks a description of the return format, but the purpose implies a list of languages, which is sufficient.
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?
Schema coverage is 100%, so baseline is 3. The description adds minimal value beyond the schema, essentially repeating the parameter names and brief descriptions. No additional constraints or formatting details are provided.
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: listing available subtitle languages for a video. It explicitly distinguishes from sibling tools like extract_subtitles by indicating that this tool is for discovering which subtitles are available.
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 implies the primary use case (knowing which subtitles can be extracted) and provides authentication configuration guidance. However, it does not explicitly state when to use this tool versus alternatives like extract_subtitles or get_video_info.
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 must provide behavioral details. It covers authentication methods (cookies_file, env variable, browser cookies) but does not disclose potential errors (e.g., no subtitles found, unsupported video) or output format. This is adequate but not fully comprehensive.
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, with a clear two-line purpose, followed by structured sections for authentication and parameters. Every sentence adds value without redundancy.
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?
For a tool with 4 parameters, no output schema, and no annotations, the description explains purpose, authentication, and parameters adequately. However, it omits the return format (e.g., timestamp list with context) and error handling, leaving some gaps for an LLM agent.
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?
Schema description coverage is 100%, so the schema already documents parameters. The description's parameter list adds minimal extra value beyond restating purposes. The authentication section provides additional context, but baseline 3 is appropriate.
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 searches keywords in subtitles and returns timestamp positions, with a usage context of quickly locating topics in a video. This distinguishes it from sibling tools like extract_subtitles (extracts full subtitles) and list_available_subtitles (lists available subtitle tracks).
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 includes a usage context ('用于快速定位视频中提到某个话题的时间点') but does not explicitly state when not to use this tool or mention alternatives. For example, it could clarify that subtitles must be available first (use list_available_subtitles) or that extract_subtitles is for obtaining full text.
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, the description carries the full burden. It discloses supported platforms, return type (complete subtitle text with timestamps), and authentication fallback hierarchy. It does not mention error handling or rate limits, but sufficiently covers expected behavior for a read operation.
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 well-structured with clear sections (purpose, platforms, return, authentication, parameters), front-loaded with the main action, and no extraneous 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?
Given no output schema, the description adequately explains return values and formatting. It covers input parameters, authentication, and supported platforms. Lacks edge-case handling but is complete for typical use.
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?
Despite 100% schema coverage, the description adds value by explicitly stating defaults (lang: auto-detect, format: 'srt') and providing authentication priority context for 'cookies_file'. This goes beyond the schema's parameter descriptions.
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 '从视频 URL 提取字幕' (extract subtitles from video URL), specifying supported platforms (YouTube, Bilibili) and distinguishing this tool from siblings like 'list_available_subtitles' and 'search_timestamp' by focusing on actual extraction.
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 provides authentication configuration priorities, offering some usage context, but does not explicitly state when to use this tool versus siblings (e.g., when to use 'list_available_subtitles' instead). No direct comparison to alternatives.
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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