Turn listening history, likes, follows, and search signals into personalized content suggestions using optional filters for type, genre, language, and discovery level.
Combine local history/likes/following/search signals into new deterministic recommendations.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| content_type | No | ||
| preference_profile | No |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description does not contradict them. The description adds the determinism trait and the source-signal scope, but it does not disclose output format, defaults, personalization bounds, or other behavioral details such as pagination or rate limits. The annotation safety profile lowers the burden, making this adequate 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the core action and scope. Every word contributes meaning, and there is no repetition of schema or annotation 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 the tool's complexity—a nested preference_profile, multiple enums, zero parameter documentation, and no output schema—the description is insufficient. It does not explain what the returned recommendations look like, how the inputs affect results, or how this tool relates to nearby siblings. Some purpose is conveyed, but an agent would have to guess at critical details before invoking it confidently.
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 compensate for undocumented parameters. It does not mention limit, content_type, or preference_profile at all, and the nested preference_profile object with seven sub-properties is left entirely unexplained. The description adds no semantic value beyond the parameter names already present in the 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 states a specific verb ('combine') and resource ('local history/likes/following/search signals') and clearly communicates that the output is recommendations. It is not a tautology, and the 'deterministic' qualifier adds useful scope. However, it does not explicitly differentiate itself from sibling tools like related_tracks or search_tracks.
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 when to use the tool: when you want recommendations derived from a combination of local history, likes, followings, and search signals. It provides no explicit when-not-to-use guidance, no mention of alternatives, and no conditions for choosing this over related_tracks or search_tracks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.