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playlist_chunk_preview

Preview how a playlist splits into write-sized chunks (default 100-URI limit or custom size) to plan batched edits before applying them, showing per-chunk ranges, counts, and boundary items.

Instructions

Read-only pagination preview: how a playlist splits into write-sized chunks (the 100-URI replace limit) or any custom size — per-chunk position ranges, first/last items, and item counts. Plan batched edits before running them. Quota: 🟢 2 GETs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
offsetNo1-based item position to start the first chunk at. Default 1
page_sizeNoItems per chunk to simulate. Default 100 (the atomic-replace limit)
max_resultsNoMax items to return (default: SPOTIFY_MCP_MAX_ITEMS env or 50)
playlist_idYesPlaylist to preview, as ID or spotify:playlist: URI
chunks_to_showNoHow many chunks to detail. Default 5
response_formatNo'concise' = human prose, 'detailed' = more fields in prose, 'json' = raw API objectconcise
Behavior4/5

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

With no annotations provided, the description carries the full burden. It clearly discloses read-only safety, the quota ('🟢 2 GETs'), and the kind of output produced (chunk ranges, first/last items, counts). This is strong behavioral disclosure for a preview-only tool, though it doesn't cover auth requirements or error cases.

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

Conciseness5/5

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

Two sentences, both information-dense with no filler. The read-only scoping is front-loaded, followed by the core behavior, the use case, and the quota. Every clause earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description fully communicates the tool's purpose, read-only nature, quota, and output shape, which is sufficient for an agent to decide whether to invoke it. It doesn't describe the exact output schema, but none exists, and the parameter defaults are documented in the input schema. A clear, complete definition for a preview utility.

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 coverage is 100%, so the baseline is 3. The description adds meaningful value beyond the schema by explaining that the default page_size corresponds to the '100-URI replace limit' and by clarifying that the output includes per-chunk position ranges, first/last items, and item counts. This connects parameters to the tool's real-world purpose.

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

Purpose5/5

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

Description uses a specific verb 'preview' with a clear resource: playlist chunking. It immediately states 'Read-only pagination preview' and explains exactly what the preview shows (chunk position ranges, first/last items, counts), distinguishing it from mutation tools like split_playlist or replace_playlist_items.

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

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells when to use this tool: 'Plan batched edits before running them', framing it as a pre-flight planning step for write operations. It doesn't name alternative tools or exclusion conditions, but the clear use case is enough to route an agent correctly in most contexts.

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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