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Nooticr

@orchyn/mcp

Official
by Nooticr

Get Post Transcript

get_post_transcript
Read-onlyIdempotent

Retrieve spoken words from a TikTok or YouTube post via its caption track. Get verbatim script, hook, or CTA before analysis, with word count or a reason if captions are unavailable.

Instructions

Get the words actually spoken in a TikTok or YouTube post by reading its caption track. Cheap and exact — use this before analyze_post when you need the script, hook wording or CTA verbatim rather than an interpretation. Returns plain text with a word count, or available:false with a reason when the post has no captions. Consumes 1 orchyn credit.Use before any analysis when the exact wording matters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesPost URL (TikTok or YouTube).
languageNoPreferred language code, e.g. 'en'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonNoWhy there is no transcript, when available is false.
languageNo
availableNofalse when the post carries no caption track.
wordCountNo
mcpCreditsNo
transcriptNo
autoGeneratedNo
Install Server

TDQS

A4.7/5.0
Behavior5/5

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

The annotations already indicate readOnly and idempotent behavior, and the description adds valuable context: it consumes 1 orchyn credit, returns plain text with a word count, and provides an available:false response with a reason when no captions exist. This goes beyond the structured annotations and gives the agent clear expectations.

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?

Every sentence is purposeful and the description is front-loaded with the core function, followed by usage guidance, return behavior, and cost. The only minor flaw is a missing space in 'credit.Use', but overall it is tight and well structured.

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?

For a low-complexity read-only tool with an output schema and full schema parameter coverage, the description fully covers what the tool does, when to use it, what to expect as output, and the cost of invoking it. No critical operational detail is missing.

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 baseline is 3. The description does not add meaningful parameter-level detail beyond what the schema already provides for 'url' and 'language'.

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?

The description states a specific action ('Get the words actually spoken'), identifies the resource (caption track of TikTok or YouTube posts), and explicitly differentiates from analyze_post by emphasizing verbatim transcript over interpretation. This makes the tool's purpose immediately distinguishable from siblings.

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?

'Use this before analyze_post when you need the script, hook wording or CTA verbatim rather than an interpretation' directly tells the agent when to choose this tool over a clear alternative. 'Use before any analysis when the exact wording matters' further reinforces the decision rule.

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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