Search-Movie
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Search-Moviefind streaming links for The Matrix"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
电影搜索工具 Search Movie
一个基于 Model Context Protocol (MCP) 构建的智能电影和电视剧资源搜索工具,支持多源搜索和链接验证。 An intelligent movie and TV series resource search tool based on Model Context Protocol (MCP), supporting multi-source search and link verification.## 工具列表 Tool List
本MCP服务封装下列工具,可让模型通过标准化接口调用以下功能。 本MCP服务封装下列工具,可让模型通过标准化接口调用以下功能。
工具 Tool | 描述 Description |
search_movie | 搜索电影或电视剧资源。返回未验证的搜索结果列表,包含标题、链接和质量信息。使用此工具获取候选资源后,请从结果中选择最匹配的链接,然后使用 validate_video_url 工具验证其可播放性。 |
validate_video_url | 验证特定视频链接的可播放性。接收一个视频播放页面的 URL 或 URL 数组,返回该链接是否可以正常播放。支持批量验证多个链接。只有通过验证的链接才能确保用户可以观看。 |
检查服务 ## Inspector
工具在线测试: https://mcp.xiaobenyang.com/inspector/1777316659782659
Online Tool test https://mcp.xiaobenyang.com/inspector/1777316659782659
Related MCP server: Movies Search Tool
服务配置 MCP Server Config
如何获取 XBY-APIKEY ? How to get XBY-APIKEY ?
访问小笨羊科技网站 https://xiaobenyang.com,注册用户即可获得APIKEY Visit XiaoBenYang website https://xiaobenyang.com, register and get the APIKEY.
SSE
{
"mcpServers": {
"电影搜索工具": {
"headers": {
"XBY-APIKEY": "<YOUR_XBY_APIKEY>"
},
"type": "sse",
"url": "https://mcp.xiaobenyang.com/1777316659782659/sse"
}
}
}STREAMABLE HTTP
{
"mcpServers": {
"电影搜索工具": {
"headers": {
"XBY-APIKEY": "<YOUR_XBY_APIKEY>"
},
"type": "streamable_http",
"url": "https://mcp.xiaobenyang.com/1777316659782659/mcp"
}
}
}STDIO
{
"mcpServers": {
"电影搜索工具": {
"command": "npx",
"args": [
"-y",
"xiaobenyang-mcp"
],
"env": {
"XBY_APIKEY": "<YOUR_XBY_APIKEY>",
"mcpId": "1777316659782659",
},
"transport": "stdio"
}
}
}
Available Tools
2 toolssearch_moviesearch_movieA
搜索电影或电视剧资源。返回未验证的搜索结果列表,包含标题、链接和质量信息。使用此工具获取候选资源后,请从结果中选择最匹配的链接,然后使用 validate_video_url 工具验证其可播放性。
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | ||
| type | Yes | ||
| season | No | ||
| episode | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it returns '未验证的搜索结果列表' (unverified search results), specifies the return format ('包含标题、链接和质量信息'), and explains the verification workflow. It doesn't mention rate limits, authentication needs, or pagination 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 perfectly concise with two sentences that each earn their place. The first sentence states the purpose and return format, the second provides crucial workflow guidance. No wasted words, front-loaded with essential information.
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 annotations, no output schema, and 0% schema description coverage for a 4-parameter tool, the description does well on purpose and workflow but leaves significant gaps. It explains the verification workflow clearly but doesn't document parameters or provide detailed behavioral context about search limitations, result formats, or error conditions.
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?
With 0% schema description coverage and 4 parameters (2 required), the description provides no information about any parameters. It doesn't explain what 'title', 'type', 'season', or 'episode' mean, their expected formats, or how they affect the search. The description fails to compensate for the complete lack of schema documentation.
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 specific action ('搜索电影或电视剧资源'), the resource ('电影或电视剧资源'), and distinguishes from the sibling tool by explaining the workflow relationship. It explicitly mentions using this tool first to get candidate resources, then using validate_video_url for verification.
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 explicit guidance on when to use this tool ('获取候选资源后') and when to use the alternative ('然后使用 validate_video_url 工具验证其可播放性'). It establishes a clear workflow sequence between the two tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_video_urlvalidate_video_urlA
验证特定视频链接的可播放性。接收一个视频播放页面的 URL 或 URL 数组,返回该链接是否可以正常播放。支持批量验证多个链接。只有通过验证的链接才能确保用户可以观看。
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes |
TDQS
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 mentions support for batch validation ('支持批量验证多个链接') and that only validated links ensure viewability, but it fails to describe critical behaviors such as error handling, rate limits, authentication needs, or what constitutes a 'valid' link. This leaves significant gaps for a tool that performs validation operations.
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 appropriately sized and front-loaded, with three sentences that each add value: stating the tool's purpose, detailing input parameters, and explaining the outcome. There is no redundant or unnecessary information, making it efficient and well-structured for quick comprehension.
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 moderate complexity (validation with batch support), no annotations, and no output schema, the description is partially complete. It covers the basic purpose and input semantics but lacks details on behavioral traits (e.g., error responses, validation criteria) and output format, which are crucial for effective tool invocation by an agent.
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 description coverage is 0%, so the description must compensate. It adds meaningful context by specifying that the 'url' parameter accepts '视频播放页面的 URL 或 URL 数组' (video playback page URLs or arrays of URLs) and clarifies its purpose for playability validation. This goes beyond the bare schema, providing essential semantic information for the single parameter.
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 purpose with specific verbs ('验证', '返回') and resources ('视频链接的可播放性', '视频播放页面的 URL 或 URL 数组'). It distinguishes itself from the sibling tool 'search_movie' by focusing on URL validation rather than content search, making the purpose unambiguous and well-defined.
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 context by stating '只有通过验证的链接才能确保用户可以观看', suggesting it should be used to ensure video playability. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., compared to 'search_movie') or any prerequisites for usage, leaving some ambiguity for the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
2 tool updates
- First observed
search_movie - First observed
validate_video_url
TDQS
The two tools have clearly distinct purposes: search_movie finds potential video resources, while validate_video_url checks their playability. There is no overlap in functionality, and an agent can easily distinguish when to use each tool based on the workflow described in the search_movie description.
Both tools follow a consistent snake_case naming pattern with clear verb_noun structure: search_movie and validate_video_url. The naming is predictable and readable, with no deviations in style or convention across the set.
With only 2 tools, the server feels thin for a movie search domain, as it lacks operations like filtering, sorting, or accessing detailed metadata. However, the tools cover a basic workflow (search and validate), making it borderline appropriate but limited in scope.
The server provides a minimal workflow for finding and verifying video links, but there are notable gaps: no tools for browsing categories, getting movie details, or managing user preferences. It covers search and validation but misses broader domain operations, leaving agents with limited capabilities.
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