recommend
Get personalized video recommendations from Douyin (TikTok China) based on user preferences and viewing history.
Instructions
recommend
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Get personalized video recommendations from Douyin (TikTok China) based on user preferences and viewing history.
recommend
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. 'recommend' gives no indication of whether this is a read or write operation, what permissions might be needed, what side effects exist, or how the tool behaves. It fails to provide any 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
While technically concise with a single word, this is under-specification rather than effective brevity. The description fails to convey necessary information, making it inefficient rather than appropriately sized. Every sentence should earn its place, but here the single word doesn't earn its place by being informative.
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 complete lack of annotations, no output schema, and a vague description, this is completely inadequate. The tool name 'recommend' could mean anything from content recommendations to system suggestions, but the description provides no context about what's being recommended, to whom, or with what criteria.
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 tool has zero parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description doesn't need to add parameter information, and while it doesn't provide any semantic context about what 'recommend' means in terms of inputs, the baseline for zero parameters is 4.
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?
Tautological: description restates name/title.
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?
There is absolutely no guidance on when to use this tool, what context it applies to, or when to choose alternatives. The description offers no information about usage scenarios, prerequisites, or exclusions.
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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curl -X GET 'https://glama.ai/api/mcp/v1/servers/BACH-AI-Tools/bachai-douyin-api-new'
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