make_spotify_uri

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

With no annotations provided, the description carries the full behavioral burden. It does disclose meaningful traits: strict validation, rejection of malformed IDs and unknown kinds, and fail-fast ordering ('before anything is built'). However, it omits the error surface (throw vs. error return), whether the tool is a pure side-effect-free function, and how the response_format parameter affects output.

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?

A single 22-word sentence that front-loads the action and resource, then appends the differentiating behavioral detail via an em-dash clause. Every word earns its place; the fail-fast phrasing adds meaning rather than padding.

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?

The description fully covers what the tool does and its key validation behavior, which is enough for a small 2-required-param utility. But with no output schema and no annotations, it leaves two meaningful gaps: the return format on success and how failures are represented. It also doesn't address edge cases like an input that already contains the 'spotify:' prefix.

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 coverage is 100%, so the baseline is 3. The description adds value by defining the relationship between parameters ('kind + ID') and by attaching validation semantics to them: id must have the correct length for its kind, and kind must be from a known set. This clarifies constraints well beyond the schema's generic 'Spotify entity ID' and 'Entity kind'.

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?

States a specific verb ('Construct'), a precise resource ('spotify: URI'), and the input shape ('from kind + ID'). The strict-validation clause with concrete rejection cases (wrong-length IDs, unknown kinds) distinguishes it from URI siblings like validate_spotify_uri, parse_spotify_uri, and format_spotify_uri, whose inputs/outputs differ.

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 intended use is implied — when you have a kind and ID and want a validated URI, use this tool. However, with a large cluster of overlapping URI tools (validate_spotify_uri, is_valid_spotify_uri, format_spotify_uri, normalize_spotify_uri, canonicalize_spotify_uri), it names no alternatives and gives no explicit when-not-to-use guidance, leaving selection partially to inference.

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