ScrapeCreators MCP Server

Clip Transcript

twitch_clip_transcript

Retrieve the spoken transcript of a public Twitch clip, using native captions first and AI transcription only as a fallback when captions are missing.

Instructions

Gets a transcript from a public Twitch clip. The endpoint checks Twitch's native captions first. Set use_ai_as_fallback to true to use AI transcription only when native captions are unavailable. Native transcripts cost 1 credit, AI transcripts cost 10 credits, and no credits are charged when no transcript is found. transcript_source is native, ai, or null. 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 Read-like POST requests do not publish to social platforms.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_transcript"
  }
}

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.
`use_ai_as_fallback`	No	Use AI transcription only when native captions are unavailable. Costs 10 credits when an AI transcript is returned. Defaults to false.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.0Oct 2, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Discloses behavior beyond the annotations: native captions are checked first, credit tiers (1 for native, 10 for AI, 0 when none found), the confirm=true gate, and clarifies that the read-like POST does not publish to social platforms – directly resolving the ambiguity created by readOnlyHint=false.

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 purpose and mechanism, then the cost/confirmation constraints. Efficient overall, though the credit/cost sentences and the final POST clarification are slightly dense for four parameters.

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 usefully defines the return indicator (transcript_source native/ai/null) and the cost/prerequisite semantics. It is nearly self-sufficient; only the exact transcript payload shape is unstated.

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 value by linking the parameters to cost and behavior (use_ai_as_fallback triggers 10-credit AI transcription) and by naming the transcript_source output values (native, ai, or null).

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 and resource with scope: 'Gets a transcript from a public Twitch clip.' An agent can distinguish it from twitch_clip (clip metadata) and from transcript tools for other platforms (kick_clip_transcript, youtube_transcript) without opening the 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?

Provides clear operational conditions: set use_ai_as_fallback to true only when native captions are unavailable, and confirm=true is required. It does not, however, explicitly compare against sibling tools or state exclusions (e.g., when to prefer twitch_clip instead).

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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