IranTools (paid)

strip_html

Origin URL: https://irantools.johnaio281.workers.dev Origin hostname: irantools.johnaio281.workers.dev Origin brand: IranTools - paid MCP server for AI agents Provider aliases: HTML stripper, Markup cleaner Family key: irantools.johnaio281.workers.dev::text Endpoint: POST /api/iran/strip_html Summary: Remove HTML tags and decode entities from a string. Keyword: strip html Keyword: remove tags Keyword: html to text Keyword: decode entities Keyword: clean markup Use case: remove HTML tags from scraped text Use case: turn a rendered page into plain text Use case: decode HTML entities in a string Source description: Remove HTML tags and decode entities from a string. Auth mode: paid

Price: $0.003 per call (USDC on Base via x402, or USDC on Tempo via MPP).

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": {
      "text": "<text>"
    },
    "name": "strip_html"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 21, 2026

TDQS

B3/5.0

Behavior2/5

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

No annotations are supplied, so the description carries the full disclosure burden, yet it says nothing about how tags are handled (script/style contents? attribute preservation? whitespace collapsing?) or what the decoded output looks like. The only real behavioral facts given are registry metadata — paid auth mode and $0.003/call pricing — which is cost context, not tool behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The payload is dominated by registry boilerplate — origin URL, hostname, brand, provider aliases, family key, endpoint, auth mode, price — plus five keywords and three use cases that restate the same idea. The one useful sentence is buried mid-block, and much of the text is redundant rather than front-loaded.

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?

For a single-parameter utility with no output schema and no annotations, the definition covers the basic purpose and plausible scenarios but omits return shape and tag/entity handling rules. It is minimally workable but leaves the agent to discover output semantics by calling it.

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 0% for the single 'text' parameter, so the description must compensate. 'from a string' confirms the input is raw string content rather than a URL or buffer, but it adds nothing about encoding expectations, size limits, or whether fragments are acceptable.

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 Summary line states a specific verb+resource: 'Remove HTML tags and decode entities from a string.' A reader instantly knows what the tool does. However, it does not name or contrast itself against any sibling (e.g., persian_normalize, unicode_normalize) that also mutate text, so differentiation is left to inference.

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?

Three use cases are provided ('remove HTML tags from scraped text', 'turn a rendered page into plain text', 'decode HTML entities in a string'), which imply when to reach for the tool. There are no explicit exclusions or alternatives, so the agent must still guess at boundaries versus the other text-cleaning siblings.

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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