Samplecut

Get transcript

get_transcript

Read-only

Fetch timestamped captions for a YouTube, TikTok, Instagram, or SoundCloud URL when the platform provides them. Use segment start_sec/end_sec (or the flat text) to choose a cut range, then call cut_audio with the same URL and those times. When available is false there are no captions — do not invent lyrics or timestamps; tell the user to pick times another way. Optional lang is a BCP-47 code (default en). No job_id is returned.

Input Schema

TableJSON Schema

Name	Required	Description
`url`	Yes	YouTube, TikTok, Instagram, or SoundCloud URL to fetch captions for
`lang`	No	Optional BCP-47 caption language (default en)
`context`	Yes	One short sentence on what the user is trying to do with this audio (for example 'ringtone from the chorus'). No links, names or personal details.
`llm_model`	Yes	The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
`conversation_id`	No	Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`lang`	Yes
`text`	Yes
`source`	Yes
`segments`	Yes
`available`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 18, 2026

changedInput schema / properties / url / description

Previous value: -"YouTube, TikTok, or Instagram URL to fetch captions for"New value: +"YouTube, TikTok, Instagram, or SoundCloud URL to fetch captions for"

Changed4 schema fields changedOct 13, 2026

addedInput schema / properties / context

Added value: +{
+  "description": "One short sentence on what the user is trying to do with this audio (for example 'ringtone from the chorus'). No links, names or personal details.",
+  "type": "string"
+}

addedInput schema / properties / conversation_id

Added value: +{
+  "description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.",
+  "type": "string"
+}

addedInput schema / properties / llm_model

Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}

changedInput schema / required

Previous value: -[
-  "url"
-]New value: +[
+  "url",
+  "context",
+  "llm_model"
+]

First observedSep 29, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already cover safety (readOnlyHint, destructiveHint=false, openWorldHint), but the description adds real behavioral context: what happens when captions are unavailable, that no job_id is returned, and the anti-hallucination constraint. This goes well beyond the structured annotations.

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?

Purpose is front-loaded in the first sentence, followed by the downstream workflow, the failure case, and the one param note. Every sentence carries non-redundant information with no waste.

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?

An output schema exists, so return values need not be explained; the description still covers the sibling handoff, the unavailable-captions case, and the lang default. Complete for an agent to invoke correctly and act on the result.

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 the schema already documents url, lang, context, llm_model, and conversation_id in detail. The description restates lang (BCP-47, default en) but adds nothing beyond the schema for any parameter, so baseline 3 applies.

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 (Fetch) and resource (timestamped captions) and names the platforms it supports (YouTube, TikTok, Instagram, SoundCloud). It also implicitly distinguishes itself from cut_audio by describing the fetch-then-cut workflow.

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

Usage Guidelines5/5

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

Explicitly routes to the sibling: use segment start_sec/end_sec then call cut_audio with the same URL and times. It also covers the failure path (available is false) with concrete guidance not to invent lyrics or timestamps and to tell the user to pick times another way.

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

All Samplecut tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?