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elcachorrohumano

Last.fm MCP Server

get_user_loved_tracks

Retrieve a user's favorite tracks from Last.fm by specifying username, with options to limit results and paginate through the collection.

Instructions

Get tracks a user has marked as loved

Args: user: Username limit: Maximum number of results to return (1-50, default: 10) page: Page number to retrieve (default: 1)

Returns: Formatted list of user's loved tracks

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
userYes
limitNo
pageNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It states the tool retrieves data ('Get tracks'), implying it's read-only, but doesn't disclose behavioral traits like rate limits, authentication requirements, error conditions, or pagination details beyond basic parameter defaults. The description adds minimal context beyond the obvious read operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and appropriately sized. It front-loads the purpose in a clear sentence, followed by organized sections for Args and Returns. Each sentence earns its place, with no redundant information. Minor improvement could be integrating the purpose more seamlessly with parameter details.

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?

Given 3 parameters, 0% schema coverage, no annotations, but an output schema exists, the description is moderately complete. It covers parameter semantics adequately and states the return value ('Formatted list'), but lacks behavioral context (e.g., authentication, errors). The output schema likely handles return structure, so the description doesn't need to detail it further.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

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. It provides clear semantics for all three parameters: 'user' as 'Username', 'limit' with range and default, and 'page' with default. This adds meaningful context beyond the bare schema, though it doesn't explain format constraints (e.g., username requirements) or pagination behavior beyond page numbers.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get tracks a user has marked as loved' - a specific verb ('Get') and resource ('tracks marked as loved'). It distinguishes from siblings like 'get_user_recent_tracks' or 'get_user_top_tracks' by specifying the 'loved' status. However, it doesn't explicitly contrast with 'unlove_track' or 'love_track' which are related but different operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 is provided. The description doesn't mention when this is appropriate compared to other user-related tools like 'get_user_info' or 'get_user_recent_tracks', nor does it specify prerequisites (e.g., whether authentication is needed). The agent must infer usage from the tool name and context alone.

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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