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mdapi — HTML to Markdown for AI agents

html_to_markdown

Convert a web page (by URL) or raw HTML into clean, LLM-ready Markdown. Readability article extraction; nav/scripts/footers stripped; tables preserved. Set render=true to execute the page in a real headless browser first — required for JS-rendered SPAs (React/Next/Vue) that return empty HTML to a plain fetch. Paid tool: $0.005 per call via x402 (USDC on Base).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoPublic http(s) URL of the page to fetch and convert
htmlNoRaw HTML to convert instead of fetching a URL
modeNoarticle (default): main content only via Readability. full: whole page.
renderNotrue: execute the page in a headless browser before converting (JS-rendered SPAs)

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that nav/scripts/footers are stripped, tables preserved, and render=true executes in a headless browser. It also mentions payment via x402. This is good, though rate limits or error handling are not covered.

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?

The description is three sentences, front-loaded with the purpose, then behavioral details, then parameter and cost info. Every sentence adds value with no redundancy.

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?

The description covers purpose, behavior, parameter context, and cost. Missing explicit return value format, but output schema is absent. Given complexity (4 params, no annotations), it is reasonably complete.

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?

Schema description coverage is 100%, giving baseline 3. The description adds value by explaining the render parameter's necessity for SPAs and the article extraction behavior, which goes beyond the schema descriptions.

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 'Convert a web page (by URL) or raw HTML into clean, LLM-ready Markdown.' This identifies the specific verb (convert) and resource (web page or raw HTML). It also mentions Readability article extraction and handling JS-rendered SPAs, which distinguishes it from siblings like extract_page.

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 provides explicit guidance on when to use the render parameter ('required for JS-rendered SPAs') and notes cost ($0.005 per call). However, it does not compare directly with sibling tools or state when not to use this tool, leaving some ambiguity.

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

A4/5.0
Disambiguation5/5

Each tool has a distinct purpose: extract_page extracts JSON metadata, html_to_markdown converts to Markdown, and service_info provides pricing details. No overlap or confusion.

Naming Consistency5/5

All names use consistent lowercase snake_case with a verb-noun pattern: extract_page, html_to_markdown, service_info. Predictable and clear.

Tool Count5/5

Three tools is well-scoped for a focused HTML-to-Markdown service. Each tool serves a necessary role without bloat or thinness.

Completeness5/5

The tool set covers the core workflows: extracting structured data, converting to Markdown, and accessing service info. No obvious gaps for the stated purpose.

Resources