MCP YouTube Intelligence

search_transcripts

Find specific content in YouTube video transcripts by searching with keywords to locate relevant snippets for analysis or research.

Instructions

Search stored transcripts by keyword. Returns matching snippets.

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": {
      "query": "<query>"
    },
    "name": "search_transcripts"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes	Search keyword or phrase
`limit`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.3Feb 18, 2026

TDQS

B3.1/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool 'returns matching snippets,' which gives some output context, but lacks details on permissions, rate limits, error handling, or whether it's read-only/destructive. For a search tool with zero annotation coverage, this is insufficient.

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?

The description is two concise sentences with zero waste—it states the action and the result directly. It's appropriately sized and front-loaded, making it easy to parse quickly.

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

Completeness3/5

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

Given the tool's moderate complexity (search with two parameters), no annotations, and no output schema, the description is minimally adequate. It covers the basic purpose and return type but lacks details on usage context, behavioral traits, and parameter nuances, leaving gaps for an AI agent.

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 50% (only 'query' has a description, 'limit' has none). The description adds no parameter semantics beyond what's in the schema—it doesn't explain 'query' further or clarify 'limit' behavior (e.g., max value, pagination). With partial schema coverage, the description doesn't compensate adequately, meeting the baseline.

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?

The description clearly states the tool's purpose with a specific verb ('search') and resource ('stored transcripts'), and mentions the return type ('matching snippets'). However, it doesn't explicitly differentiate from sibling tools like 'get_transcript' or 'search_youtube', which would require a 5.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like 'get_transcript' (which might retrieve full transcripts) or 'search_youtube' (which might search YouTube content). It only states what the tool does, not when it's appropriate.

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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