Your Spotify MCP Server

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 describes what the tool does (analyze patterns) but omits critical behavioral traits: whether it's read-only or mutative, what data sources it accesses, potential rate limits, authentication needs, or output format. The examples hint at query-like behavior but don't clarify operational constraints.

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, starting with the core purpose followed by discovery scope and example queries. Each sentence adds value: the first states the action, the second clarifies temporal dimensions, and the examples illustrate use cases. No redundant or wasteful phrasing 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?

Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It covers the 'what' and provides usage examples but lacks details on behavioral traits, output format, or differentiation from siblings. Without annotations or output schema, the agent must guess about safety, performance, and results.

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 100%, providing clear documentation for all three parameters (pattern_type with enum, start_date, end_date). The description adds no parameter-specific semantics beyond what the schema already states, such as explaining how 'day_and_time' differs from other pattern types or date range implications. Baseline 3 is appropriate given the schema's completeness.

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: analyzing listening patterns over time by hour, day, or month. It specifies the resource (listening patterns) and verb (analyze) with concrete examples. However, it doesn't explicitly differentiate from sibling tools like 'get_listening_timeline' or 'search_listening_history', which likely provide related but different 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 implies usage through example queries (e.g., 'What time of day do I listen to music most?'), suggesting it's for temporal pattern analysis. However, it lacks explicit guidance on when to use this tool versus alternatives like 'compare_listening_periods' or 'get_listening_timeline', leaving the agent to infer context from tool names alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.