wetv-mcp

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a distinct purpose: search_content handles targeted queries, get_trending and get_popular are separated by time horizon, get_content_detail provides specifics, get_episodes lists episodes, and get_recommendations offers personalized suggestions. The overlap between trending/popular/recommendations is clearly explained in descriptions.
Naming Consistency4/5
All tools use a consistent verb-first pattern ('search' or 'get'), and most follow verb_noun structure. However, get_trending and get_popular omit the noun, using an adjective instead, which is a minor deviation from the otherwise uniform pattern.
Tool Count5/5
Six tools is a well-scoped size for a video content discovery server. Each tool serves a necessary function—search, browse, detail, episodes, and recommendations—without redundancy or bloat.
Completeness5/5
The tool set covers the complete user journey: discovering content (search, trending, popular, recommendations), understanding it (detail), and accessing episodes. There are no obvious gaps for the stated purpose of exploring WeTV/Tencent Video content.
Average 4/5 across 6 of 6 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
- Last stable release on April 6, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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, so the description must disclose behavioral traits. It does not mention that this is a read-only operation, whether results are paginated, or any limitations (e.g., no results handling). It only lists search capabilities, lacking depth in behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long and front-loaded with the core function. Every word adds value, and there is no redundant or filler content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 6-parameter search tool with no output schema, the description is adequate but has gaps. It does not mention pagination, response format, or that region/language filters exist in the schema, which an agent might need for effective invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description repeats what the schema already provides (e.g., search by title, actor, director, genre, keywords; Chinese and English support) without adding new meaning beyond the parameter definitions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches for specific content types (dramas, variety shows, anime, movies, documentaries) on WeTV and Tencent Video. It uses the specific verb 'search' and lists search dimensions (title, actor, director, genre, keywords), distinguishing it from sibling tools like get_trending and get_content_detail.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
While the description implies this tool is for searching specific content, it does not explicitly state when to use it versus alternatives such as get_trending for discovery. No exclusions or preferred contexts are mentioned, but the purpose is self-evident.
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 burden of behavioral disclosure. It states the tool is 'personalized' and an 'AI drama advisor', implying algorithmic curation, but does not disclose whether results are deterministic, how many results are returned, or what data is used for personalization. There is no mention of side effects, but as a recommendation tool, it is likely read-only. This is acceptable but lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with the core function. Each sentence earns its place: the first defines the tool, the second provides practical examples, and the third clarifies language support. No fluff or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 5 parameters, no required fields, and no output schema. The description and schema cover input parameters well, but the description does not explain what the response looks like (e.g., a list of items with IDs and titles), which is important for an agent to chain subsequent calls like get_content_detail. This is a notable gap given the absence of an output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters. The description adds a general note about supporting 'both Chinese and English genre/mood keywords' and references 'viewing context', which aligns with the 'mood' parameter. This adds slight value beyond the schema but doesn't introduce new parameter semantics that aren't already present.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Get personalized content recommendations from WeTV/Tencent Video based on genre preferences, mood, or viewing context.' It uses a specific verb ('Get') and resource ('content recommendations'), and distinguishes itself from sibling tools like search_content and get_trending by emphasizing personalized, context-based suggestions. The 'AI drama advisor' phrasing reinforces its unique role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides concrete use-case examples like 'what should I watch tonight?' and 'recommend something for a date', making it clear when to use the tool. However, it does not explicitly mention when not to use it or name alternative tools (e.g., get_trending for popularity-based picks, search_content for specific searches), leaving some implicit differentiation.
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 disclose behavioral traits. It does mention the output fields, which helps set expectations, but it does not mention pagination behavior (page/per_page) or any prerequisites beyond providing a content_id. It doesn't contradict any annotations since none exist, but it leaves some behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences, front-loaded with the essential purpose and followed by a focused list of return fields. No redundant or unnecessary information is present.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 3-parameter tool with no output schema, the description lists all key return fields, which is helpful. However, it stops short of explaining how pagination works or how episodes are ordered, which would make it more complete. Overall, it covers the main use case adequately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds context about content types and platform, but it doesn't significantly enrich the meaning of parameters beyond what the schema already provides. The schema already defines content_id, page, and per_page well.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with a specific verb ('Get') and resource ('episode list'), and specifies the content types and platform ('specific drama, anime, or variety show on WeTV/Tencent Video'). It also lists the returned fields (episode numbers, names, durations, air dates, play links with subtitle info), which differentiates it from siblings like get_content_detail.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a clear context for use: retrieving episodes for a specific show, which implies it should be used when episode details are needed. It doesn't explicitly mention alternative tools or exclusions, but the stated scope is sufficient to guide an agent in most cases.
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?
No annotations are provided, so the description carries the full burden for behavioral disclosure. It does explain the ranking algorithm (views × rating) which adds useful context, but it does not disclose response structure, pagination behavior, or any potential side effects. Since this is a read-only list operation, explicit safety info is less critical, but the absence of any note about return format is a gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long and front-loads the core purpose, immediately followed by the distinguishing comparison with trending. Every word contributes value with no repetition or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description should ideally clarify what the response contains. It does not mention the return structure or fields, nor does it explain the behavior of the `limit` parameter beyond what the schema already says. However, the tool's simplicity and the explicit differentiation from trending make it reasonably complete for an agent to invoke, though not fully self-describing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Parameter descriptions in the schema cover all three parameters (type, limit, region) with enums and defaults, achieving 100% coverage. The description itself does not add extra parameter-level detail beyond the ranking context, which is acceptable given the high schema coverage; thus the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves the most popular content from WeTV and Tencent Video, ranked by a combined popularity score (views × rating). It also explicitly distinguishes itself from the sibling tool `get_trending`, making its unique 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 Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly differentiates between 'popular' and 'trending' by stating that popular shows all-time best-performing content while trending is different. This gives the agent a clear basis for selecting this tool over the alternative `get_trending`.
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 that results are ranked by a real-time heat index and come from two platforms, and it enumerates the content types. While it does not mention authentication, response format, or side effects, for a read-only trending fetcher it provides substantial behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, with the first sentence front-loading the action and resource, and the second giving a concrete use case. There is zero fluff or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description still manages to convey the essence of what is returned (hottest content types ranked by heat index) and the platforms involved. It could mention the result structure more explicitly, but for a simple list-fetch tool with three optional parameters, it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides 100% coverage with descriptions for all three parameters, so the baseline is 3. The tool description adds minimal semantic value beyond the schema, only reinforcing the ranking concept. It does not elaborate on filter behavior beyond what's already in the parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Get' and identifies a clear resource: trending/hot content on WeTV and Tencent Video. It further specifies the content types and ranking method, distinguishing it from siblings like get_popular by emphasizing the real-time heat index and specific platforms.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear use case ('what's hot right now' or 'what should I watch'), making it easy for an agent to know when to select this tool. However, it does not explicitly contrast with alternatives like get_popular, so it stops short of a 5.
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?
No annotations are provided, so the description carries full burden. It discloses the return contents (synopsis, cast, rating, etc.) and implies a read-only operation, but does not disclose potential specifics like authentication requirements, data freshness, or error conditions. This is acceptable but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, two sentences, front-loaded with the core purpose, then details. Every word earns its place without unnecessary fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description lists the key return fields (synopsis, cast, rating, episode count, update schedule, play links, regions/subtitles), providing a clear picture of what the tool returns. It could mention error handling or authentication, but for a simple single-parameter lookup, it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a description for the 'id' parameter, but the tool description adds meaningful provenance ('obtained from search_content or get_trending') and a concrete example ('qyn3'), which goes beyond the schema and helps the agent acquire the correct ID.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action ('Get detailed information') and resource ('specific drama, movie, variety show, or anime on WeTV/Tencent Video'). It distinguishes itself from sibling tools by focusing on a specific content item rather than searches or lists, and enumerates the types of details returned.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for obtaining comprehensive details about a single content item, contrasting with sibling tools for searching, trends, popularity, episodes, or recommendations. It provides clear context but does not explicitly name alternatives or state when not to use this tool.
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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