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

With no annotations provided, the description carries full burden but only states it 'creates a new playlist and adds tracks'. It lacks behavioral details such as authentication requirements, rate limits, error handling, or whether the operation is idempotent, which are critical for a mutation tool.

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 front-loaded with the core purpose in the first sentence, followed by a structured 'Args' list that efficiently details parameters. Every sentence adds value without redundancy, making it easy to scan and understand quickly.

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?

For a mutation tool with no annotations and no output schema, the description covers the basic operation and parameters adequately but lacks critical context like return values, error cases, or side effects. It's minimally viable but has clear gaps in behavioral transparency.

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 description includes an 'Args' section that explains each parameter's purpose (e.g., 'name: Name of the playlist', 'public: Whether the playlist should be public'), adding meaningful context beyond the schema's 0% description coverage. It clarifies defaults and optionality, compensating well for the schema gap.

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 verb 'Create' and resource 'new playlist on Spotify', specifying that tracks are added to it. It distinguishes from siblings like 'update_playlist' by focusing on creation rather than modification, though it doesn't explicitly contrast with 'get_track_uris'.

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 no guidance on when to use this tool versus alternatives like 'update_playlist' or 'get_track_uris'. It mentions adding tracks but doesn't specify prerequisites (e.g., needing track URIs first) or contextual constraints, leaving usage decisions ambiguous.

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

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

With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it performs a lookup operation (implied read-only), handles multiple songs via a list, skips unfound songs (partial success behavior), and returns a list of URIs. It doesn't mention rate limits, authentication needs, or error handling details, but covers core functionality adequately.

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: the first sentence states the purpose, followed by clear sections for Args and Returns with bullet-point-like formatting. Every sentence adds value, with no wasted words, making it easy to scan and understand.

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 1 parameter with no schema coverage and no output schema, the description is mostly complete: it explains the input format, output format, and behavior (skipping unfound songs). It could improve by mentioning authentication requirements or rate limits, but for a lookup tool with simple parameters, it provides sufficient context for effective use.

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 input schema has 0% description coverage, so the description fully compensates by detailing the 'songs' parameter: it's a list of dictionaries with required 'name' and 'artist' keys, includes an example, and explains the structure. This adds significant meaning beyond the bare 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 clearly states the tool's purpose with a specific verb ('look up') and resource ('Spotify track URIs'), and distinguishes it from sibling tools (create_playlist, update_playlist) by focusing on retrieval rather than playlist manipulation. It explicitly mentions what it does: converting song information into Spotify URIs.

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 specifying it's for 'a list of songs' and provides an example, but doesn't explicitly state when to use this tool versus alternatives like search or when not to use it. It differentiates from siblings by function, though not with explicit 'use this when...' guidance.

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

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

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It implies mutation ('update') but doesn't disclose critical traits like authentication requirements, rate limits, whether changes are reversible, or what happens to unspecified fields. This is inadequate for a mutation tool with zero annotation coverage.

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 a clear purpose statement. The parameter explanations are structured in a list format, making it easy to scan, though it could be slightly more concise by integrating the parameter details into the opening sentence.

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 (mutation with 5 parameters), lack of annotations, and no output schema, the description is incomplete. It misses critical context like authentication needs, error handling, return values, and usage scenarios, leaving significant gaps for an AI agent to operate effectively.

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 description adds significant value beyond the input schema, which has 0% description coverage. It explains the meaning of all 5 parameters (e.g., 'playlist_id' as Spotify ID, 'track_uris' as replacement list) and clarifies optionality, compensating well for the schema's lack of descriptions.

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 verb 'update' and resource 'Spotify playlist's details and/or tracks', making the purpose explicit. However, it doesn't distinguish this tool from its sibling 'create_playlist' beyond the obvious 'existing' vs 'new' difference, missing explicit differentiation.

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?

No guidance is provided on when to use this tool versus alternatives like 'create_playlist' or 'get_track_uris'. The description lacks context about prerequisites, such as needing playlist ownership or specific permissions, and doesn't mention any exclusions or best practices.

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