extract_tables_from_html
Extract all tables from HTML as structured data. Returns: {tables: [{headers, rows, row_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
B3.3/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must carry the burden. It discloses the return format (headers, rows, row_count) but does not mention behavior like error handling for missing tables or malformed HTML.
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 very concise, with one sentence and a clear return type definition. No wasted words, though it could be slightly more structured (e.g., listing use cases).
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 low complexity (1 parameter, no output schema), the description is minimally adequate. It specifies the return structure but lacks details on edge cases like no tables found or nested tables.
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?
The only parameter 'html' is a string, but the description does not add any meaning beyond the schema. With 0% schema description coverage, the description should clarify expected format, encoding, or size limits.
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 'extract' and the resource 'tables from HTML', distinguishing it from sibling tools like html_to_markdown and html_to_text. It specifies the output as structured data.
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. For example, it should mention that this is for extracting tables, while siblings handle other HTML conversions.
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.