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get_sections

Fetch all sections (clips) of a track by slug, returning each section’s title, description, and available YouTube, X/Twitter, Farcaster, and TikTok URLs.

Instructions

Get the sections (clips) for a track, with platform URLs.

Returns each section's title, description, and URLs for YouTube, X/Twitter, Farcaster, and TikTok where available. Sections are independently published clips from multi-section compositions.

Args: slug: Track slug from list_tracks (e.g. "rusty-electrics")

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "slug": "<slug>"
    },
    "name": "get_sections"
  }
}

Input Schema

NameRequiredDescriptionDefault
slugYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.0.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are present, so the description carries the burden. It discloses the returned fields (title, description, platform URLs for YouTube, X/Twitter, Farcaster, TikTok) and the nature of sections, but says nothing about permissions, rate limits, pagination, or behavior for unknown slugs.

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?

Front-loaded with the core action and return contents, then a compact Args block. Every sentence adds information, though the 'Args:' formatting is slightly redundant for a single parameter.

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?

For a single-parameter read tool with an output schema, the description is nearly complete: it covers purpose, return contents, and parameter provenance. The main gap is the lack of explicit differentiation from the sibling list_sections.

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 0%, so the description must compensate. It does by identifying the parameter as a 'Track slug from list_tracks' and giving an example ('rusty-electrics'), which adds provenance and format meaning beyond the bare string schema.

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?

States a specific verb and resource ('Get the sections (clips) for a track') and clarifies what sections are ('independently published clips from multi-section compositions'). It does not explicitly distinguish itself from the sibling list_sections, which keeps it from a 5.

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

Usage Guidelines3/5

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

Provides useful chaining guidance by saying the slug comes from list_tracks, with an example format. However, it gives no guidance on when to use this versus list_sections or get_track, and no exclusions or preconditions.

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