refinery-mcp

clean_html

Strips and normalizes raw HTML to produce clean, LLM-ready text, with optional extraction of hashtags and mentions, reducing token usage for AI agents.

Instructions

Clean raw HTML that your agent, crawler, or browser session already fetched.

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": {
      "html": "<html>"
    },
    "name": "clean_html"
  }
}

Input Schema

Name	Required	Description
`html`	Yes	Raw HTML to strip and normalize.
`extractHashtags`	No
`extractMentions`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.6Jul 29, 2026

TDQS

B3.4/5.0

Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It only states 'Clean' and from the parameter description 'strip and normalize.' It does not detail what cleaning entails (e.g., removal of scripts, styles), side effects, or error conditions, leaving significant gaps.

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?

The description is a single concise sentence, front-loaded with the key action. It avoids verbosity, though it could include more detail without becoming overly long. Still, it is efficient.

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?

Given the tool has three parameters, no output schema, and low schema coverage, the description is insufficient. It provides no information about return values, default behaviors of boolean parameters, or any preconditions, making it incomplete for an agent to use confidently.

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 only 33% (only the 'html' parameter has a description). The description fails to explain the purpose of the 'extractHashtags' and 'extractMentions' boolean parameters, leaving the agent to guess. With such low coverage, the description should compensate but does not.

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 clearly states 'Clean raw HTML' with a specific verb and object, and specifies the source (agent, crawler, browser session). It distinguishes this tool from sibling tools like clean_url and estimate_savings by focusing on HTML cleaning.

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

Usage Guidelines4/5

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

The description tells when to use this tool: when you have raw HTML from fetching. It does not provide explicit exclusions or alternatives, but the context of 'already fetched' implies a specific scenario, which is helpful.

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

Deploy Server