ViralScout MCP

normalize_transcript

Cleans pasted transcripts or caption blocks for short-form content analysis, optionally removing speaker labels and preparing text for downstream hook, retention, and script review.

Instructions

Clean a pasted transcript or caption block for downstream short-form content analysis.

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": {
      "transcript": "<transcript>"
    },
    "name": "normalize_transcript"
  }
}

Input Schema

Name	Required	Description	Default
`transcript`	Yes
`removeSpeakerLabels`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 28, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden, and it does not disclose what 'clean' actually does (whitespace, timestamps, punctuation, speaker labels), whether the operation is deterministic, or how the input is transformed. For a text-mutation tool with zero annotation coverage this is a substantial gap.

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?

A single front-loaded sentence with no filler, stating the action and the downstream purpose. Efficient, though the brevity comes at the cost of the missing specifics noted elsewhere.

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

Completeness2/5

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

With no annotations, no output schema, and 0% parameter description coverage, the description leaves too much unspecified for a transformation tool: the nature of the cleaning, the effect of removeSpeakerLabels, and any output expectations are all absent.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate. It hints that the input is a 'pasted transcript or caption block' (mapping loosely to the transcript parameter) but says nothing about the removeSpeakerLabels boolean or its default behavior.

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?

Specific verb (clean) plus resource (pasted transcript or caption block) and target use (downstream short-form content analysis). It is clearly distinguishable from the analyze_*/generate_* siblings, though the word 'clean' leaves the actual transformation undefined.

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?

The phrase 'for downstream short-form content analysis' implies this is a preparatory step before the analysis tools, which is useful positional context. However, it never states when to use it versus alternatives, nor any exclusions or prerequisites.

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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