Read-only remote MCP connector for AI short drama search and recommendation. It supports keyword search, hot drama ranking, detail lookup, preference-based recommendation, and similar drama search.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 2.9/5 across 5 of 5 tools scored.
Each tool targets a distinct use case: search, detail, similar, hot list, and recommendation. No overlapping purposes.
All tools follow a consistent verb_noun pattern in snake_case, e.g., find_similar_short_drama, get_hot_short_drama, search_short_drama.
With 5 tools, the set is well-scoped for browsing and discovering short dramas. Each tool adds unique value without redundancy.
Covers search, detail, recommendations, similar items, and trending lists. Minor gap might be missing a 'list all categories' tool, but search covers categories via tags.
find_similar_short_dramaFind similar short dramasCInspectFind similar short dramas by id, title, or tags.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | ||
| tags | No | ||
| limit | No | ||
| title | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits. It implies a read operation ('find') but does not explicitly state read-only nature, potential side effects, authentication requirements, rate limits, or output characteristics. The lack of such details leaves ambiguity for the agent.
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 very concise, fitting a single sentence. It front-loads the main idea. However, it is arguably too minimal and lacks any structure (e.g., sections or bullet points) that could improve readability.
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 has 4 parameters, no output schema, no annotations, and several sibling tools, the description is incomplete. It does not explain what 'similar' means, result ordering, response format, or how it differs from searching or recommending. Key behavioral and usage information is missing.
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 0%, so the description should compensate. It mentions id, title, and tags as search criteria but does not explain their semantics (e.g., combination logic, match behavior) and omits the 'limit' parameter entirely. The description adds minimal value beyond the parameter names.
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 action (find similar) and the resource (short dramas), and specifies the search criteria (by id, title, or tags). However, it does not differentiate this tool from siblings like search_short_drama or recommend_short_drama, which may overlap in functionality.
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 no guidance on when to use this tool versus alternatives. It does not mention prerequisites, limitations, or scenarios where other tools would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_hot_short_dramaGet hot short dramasCInspectReturn a hot short drama ranking list.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| category | No | 全部 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist; the description fails to disclose any behavioral traits such as data freshness, pagination, authentication, or what constitutes 'hot'. Minimal disclosure.
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?
One sentence is concise but omits critical information; it is under-specified rather than efficiently packed.
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 no output schema and two parameters, the description should at least hint at return structure or ranking criteria. It is incomplete 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?
Schema coverage is 0%—the description does not explain parameters like limit or category beyond their schema defaults. No added meaning about valid category values or effect of limit.
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 uses a specific verb 'Return' and resource 'hot short drama ranking list', clearly distinguishing it from sibling tools like find_similar_short_drama or recommend_short_drama.
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?
No guidance on when to use this tool versus alternatives like search_short_drama or get_short_drama_detail. No when-not or context provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_short_drama_detailGet short drama detailCInspectGet short drama detail by id. It never returns net-disk direct links.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden but only mentions one negative behavior (does not return net-disk links). Missing details on safety, authentication, or full response behavior.
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 concise with two sentences front-loading the main purpose, but the second sentence could be integrated or expanded. No unnecessary words.
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 lack of output schema and siblings, the description fails to explain the return value structure and when to use this tool over others. The tool is simple but the description is incomplete.
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 schema has 0% coverage, and the description adds no semantic meaning to the 'id' parameter beyond stating it's used to retrieve a detail. Important context like what the id represents is omitted.
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 action 'Get short drama detail' and specifies the method 'by id', distinguishing it from sibling tools that operate without a specific id parameter.
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 does not provide any guidance on when to use this tool compared to alternatives like find_similar_short_drama or search_short_drama, relying solely on the tool name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recommend_short_dramaRecommend short dramasBInspectRecommend short dramas from a plain-language preference without calling an LLM.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| preference | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only discloses one behavioral trait: 'without calling an LLM'. It does not mention safety (e.g., read-only or destructive), side effects, or response characteristics.
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 a single concise sentence, front-loaded with the core action and constraint. Every word is necessary and no extraneous information is present.
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 tool has 2 parameters, no output schema, and no annotations. The description does not explain what the tool returns (e.g., titles, IDs, summaries), nor any limitations beyond the no-LLM constraint. For a recommendation tool, this is insufficient for an agent to fully understand invocation behavior.
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 0%, so the description must clarify parameters. It implies 'preference' is the plain-language input but gives no details about 'limit' (controls count) or expected format of preference. The sentence adds minimal meaning beyond schema.
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 action 'recommend', the resource 'short dramas', the input 'plain-language preference', and a distinguishing constraint 'without calling an LLM'. This differentiates it from sibling tools like 'search_short_drama' (keyword search) and 'find_similar_short_drama' (similarity-based).
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 use when a plain-language preference is available and when avoiding LLM calls is desired. However, it does not explicitly state when to use this tool versus alternatives, nor does it specify exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_short_dramaSearch short dramasCInspectSearch short dramas by keyword, category, and tags.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | ||
| tags | No | ||
| keyword | No | ||
| category | No | ||
| pageSize | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only states that the tool searches, without revealing behavioral traits such as pagination, read-only nature, or result format. The read behavior is implied but not explicit.
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 a single, compact sentence that conveys the core purpose efficiently. No wasted words; it is front-loaded with action and resource.
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 has 5 parameters, no output schema, and no annotations, the description is too minimal. It does not cover pagination, default values, or results structure, making it insufficient for complete agent guidance.
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 coverage is 0%, so the description must explain parameters. It mentions keyword, category, and tags, but omits page and pageSize. These are important for pagination, so the explanation is incomplete.
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 action 'Search' and the resource 'short dramas', and specifies the search dimensions (keyword, category, tags). It distinguishes from siblings by being a generic search, but could be more precise about scope (e.g., pagination is implied but not stated).
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?
No guidance on when to use this tool versus alternatives like find_similar_short_drama or recommend_short_drama. The description lacks context about appropriate usage scenarios or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
