ScrapeCreators MCP Server

Clip

twitch_clip

Fetch detailed Twitch clip data by URL, including metadata, view counts, game and broadcaster details, direct playback video URLs, and more clips from the same channel.

Instructions

Fetches detailed data for a Twitch clip by URL, including metadata and direct video URLs. Returns clip id, slug, url, embedURL, title, viewCount, language, durationSeconds, game info, broadcaster details with follower count, thumbnailURL, and videoQualities at multiple resolutions with a signed videoURL for playback. Also includes additional clips from the same broadcaster. Potentially consumes paid API credits; requires confirm=true. Read-like POST requests do not publish to social platforms.

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": {
      "url": "<url>"
    },
    "name": "twitch_clip"
  }
}

Input Schema

Name	Required	Description
`url`	Yes	Twitch clip URL
`account`	No	Named private ScrapeCreators account; selects credentials, not a remote account ID.
`confirm`	No	Must be true for the specific approved credit-consuming research call.
`cache_max_age`	No	If we have a response in the cache that is this many days old or newer, return the cached response (0 credits, with "cached": true and a "cached_at" timestamp). Otherwise, scrape a live result (1 credit). [See the Caching page for details.](https://docs.scrapecreators.com/caching)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.0Oct 2, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations declare readOnlyHint=false and openWorldHint=true, and the description usefully explains the apparent inconsistency ('Read-like POST requests do not publish to social platforms') while disclosing credit cost and the confirm gate. It does not cover pagination or error behavior, but that is minor against the 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.

Conciseness4/5

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

Front-loaded with the purpose, then one dense sentence of return fields, then a short credit/confirm caveat. The return-field enumeration is long but earns its place because there is no output schema.

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?

With no output schema, the description compensates by listing the returned payload fields (viewCount, durationSeconds, broadcaster follower count, videoQualities with signed URLs) and covers the credit/confirm mechanics. Complete enough for an agent to call correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so url, account, confirm, and cache_max_age are already documented in the schema; the description adds only the fact that lookup is by URL. Baseline 3 applies since the schema carries the parameter burden.

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 ('Fetches detailed data for a Twitch clip by URL') and enumerates exactly what comes back, so an agent can distinguish it from twitch_profile or twitch_user_videos. It does not explicitly name the nearest sibling, twitch_clip_transcript, which is the main missing differentiator.

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?

It gives real operational guidance ('Potentially consumes paid API credits; requires confirm=true'), which tells the agent a precondition before calling. It never says when to prefer this over twitch_clip_transcript or kick_clip, so choice between clip-data tools is left to inference.

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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