CleanWeb x402 — Smart Web Scraping & YouTube AI Agent

clean_text_raw

Extract clean, tag-free plain text from any webpage for RAG embedding and vector indexing. Returns title, word count, and token metrics.

Instructions

Extracts pure, tag-free plain text optimized for RAG embedding and vector indexing (0.001 USDC).

Usage Guidelines:

Use this tool when ingesting raw webpage text directly into vector databases (Pinecone, Chroma, Qdrant).
Returns: Page title, word count, clean text, and token metrics.

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": "clean_text_raw"
  }
}

Input Schema

Name	Required	Description	Default
`url`	Yes	The target website URL to extract raw text from.
`auth_token_or_tx`	No	Optional x402 auth token or tx hash.

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.2.6Sep 11, 2026

TDQS

B3.4/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses the monetary cost (0.001 USDC) and that the operation returns page title, word count, text and token metrics, which suggests a read-only extraction. It still omits whether the optional auth_token_or_tx is ever required, any rate limits, or failure modes.

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?

Well front-loaded: purpose in the first clause with price attached, then a short labeled usage block. Every line is short and scannable, with only a minor redundancy in restating return fields that an output schema already covers.

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?

An output schema exists, so return-value explanation is not strictly needed, and cost context is a genuine addition. The main gap is sibling disambiguation within a crowded family of clean_* tools and any auth requirement for the optional token parameter, leaving the definition adequate but not complete.

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 url and auth_token_or_tx parameters are already documented in the schema. The description adds no syntax, format, or auth semantics beyond it, so the baseline 3 applies.

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?

States a specific verb and resource ('Extracts ... plain text') and adds the intended downstream use ('optimized for RAG embedding and vector indexing'). However, it never distinguishes itself from the sibling clean_web_content, which appears to overlap heavily, so an agent cannot tell the two apart from the text alone.

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?

Gives implied context ('when ingesting raw webpage text directly into vector databases') which is a real usage signal. But it offers no when-not guidance and, critically, does not route the agent to or away from clean_web_content, clean_pdf_research, or clean_youtube_transcript, so the alternative-selection decision is left unresolved.

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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