html_to_text
Strip all HTML tags and return clean plain text. Returns: {text, word_count}
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| html | Yes |
Htmlpdf Transform Mcp
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
A4/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully explains the transformation (strip HTML tags, return plain text and word count). It is straightforward but could note edge cases like scripts.
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?
Two sentences, front-loaded with action 'Strip all HTML tags', no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Sufficient for a simple tool: covers input purpose and output format. Could mention limitations like large input handling.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains the single 'html' parameter's purpose (HTML to strip). Despite 0% schema description coverage, it adds essential meaning.
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 the verb 'strip' and resource 'HTML tags to plain text', distinguishing it from siblings like html_to_markdown.
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?
No guidance on when to use this tool versus alternatives (e.g., html_to_markdown). The description simply describes the action without context.
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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TDQS
B3.3/5.0
Disambiguation5/5
Each tool has a clearly distinct purpose: extracting tables, health check, converting HTML to markdown or text, and converting JSON to markdown table. No functional overlap.
Naming Consistency4/5
Most tools follow a pattern (source_to_target or verb_noun), and all use snake_case. One exception: 'health_check' is a noun_noun instead of verb_noun, but it's a minor deviation.
Tool Count4/5
With 5 tools, the count is reasonable for a focused server. It covers core HTML transformations, though PDF tools are missing from the name 'Htmlpdf'.
Completeness2/5
The server name implies both HTML and PDF handling, but there are no PDF-related tools. Significant gaps exist in the stated domain, limiting agents' ability to perform PDF operations.