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thenavidm

ScrapeCreators MCP Server

by thenavidm

Get Song Details

tiktok_get_song_details

Fetch detailed metadata for a TikTok sound or song by clipId, including title, artist, album, duration, usage count, cover art, and play URL. Requires confirm=true for credit-consuming calls.

Instructions

Fetches detailed metadata for a specific TikTok sound or song by its clipId. Returns music_info with title, author, album, duration, user_count (number of videos using this sound), play_url, cover art, and artist details. Use the clipId from a sound URL or from the popular songs endpoint. Potentially consumes paid API credits; requires confirm=true. Read-like POST requests do not publish to social platforms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
clipIdYesThis is a little confusing because this isn't songId like you'd think. It is the clipId. I guess because you can clip different portions of a song 🤷‍♂️
accountNoNamed private ScrapeCreators account; selects credentials, not a remote account ID.
confirmNoMust be true for the specific approved credit-consuming research call.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

A4.7/5.0
Behavior5/5

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

Adds traits beyond the annotations: it warns that the call 'potentially consumes paid API credits', requires confirm=true, and clarifies that the read-like POST 'does not publish to social platforms'. This last point usefully explains the otherwise puzzling readOnlyHint=false, reconciling the annotation with the tool's read-like nature rather than contradicting it.

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?

Four tight sentences with the purpose front-loaded, then return shape, then input sourcing, then cost/side-effect caveats. Every sentence carries distinct information and nothing is redundant.

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

Completeness5/5

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

With no output schema, the description steps in to enumerate the returned music_info fields and their meanings (including user_count). Combined with the credit/confirm caveats, an agent has everything needed to call it correctly.

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, but the description adds real value by explaining the provenance of clipId (sound URL or popular songs endpoint) on top of the schema's own note about the confusing clipId-vs-songId naming. It also reinforces the confirm=true requirement in context.

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?

States a specific verb ('Fetches'), a precise resource ('detailed metadata for a specific TikTok sound or song'), and the key it is looked up by (clipId). It is clearly distinguishable from related siblings like tiktok_tiktoks_using_song or tiktok_get_popular_creators without opening any schema.

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?

Tells the agent where the required input comes from ('from a sound URL or from the popular songs endpoint'), which is actionable context. It does not name exclusions or alternatives (e.g., versus the tiktoks_using_song sibling), so it stops short of explicit when-not guidance.

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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