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igorsaevets

@page2ai/mcp

by igorsaevets

Convert Page to Markdown

page_to_markdown
Read-only

Converts any web page URL into clean Markdown for LLM context, preserving headings, code blocks, and tables while stripping ads and navigation. Runs locally with no external API calls.

Instructions

Fetch a web page URL and convert it to clean Markdown optimized for LLM context. Preserves headings, code blocks (with language hints), links, and tables; strips ads, navigation, and cookie banners. For Mintlify docs (docs.anthropic.com, OpenAI platform, Vercel, Stripe, etc.) tries the URL.md convention first for cleanest output. Discovers tab groups statically and emits each panel as a ### Tab: {label} section instead of concatenating (prevents Python+TypeScript examples merging into one broken block). Zero external API calls — parses locally via linkedom.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesAbsolute HTTP or HTTPS URL of the web page to convert. Blocks private ranges, loopback, and cloud metadata endpoints.
timeout_msNoAbort the fetch after N milliseconds. Range: 1000-60000.
include_imagesNoInclude image references (![alt](src)) in the output.
include_frontmatterNoPrepend a YAML frontmatter block with title, source URL, capture timestamp, language, description, and canonical URL.
Behavior5/5

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

The description fully discloses the tool's behavior beyond annotations: it performs a fetch and local conversion, is read-only (consistent with readOnlyHint=true), handles Mintlify docs with a URL.md convention, and processes tab groups statically. There is no contradiction with annotations.

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 concise yet comprehensive, front-loading the core purpose in the first sentence. Every sentence adds distinct value: preservation/stripping details, Mintlify handling, tab group treatment, and technical approach. 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?

Given the tool's complexity (4 parameters, no output schema, no sibling tools), the description adequately covers input behavior, special cases, and output format (clean Markdown with preserved elements). It could mention the default parameter values from the schema but is otherwise sufficient.

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?

The input schema has 100% coverage with descriptions for all 4 parameters, meeting the baseline. The description adds value by explaining overall behavior but does not provide additional parameter-specific semantics beyond the schema. Thus, a score of 3 is appropriate.

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 tool's purpose: 'Fetch a web page URL and convert it to clean Markdown optimized for LLM context.' It specifies what is preserved (headings, code blocks, links, tables) and what is stripped (ads, navigation, cookie banners), making the action and result unambiguous.

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 good context for when to use this tool (converting web pages for LLM context, especially Mintlify docs) and explains its technical approach (local parsing, no external API calls). However, it does not explicitly state when not to use it or mention alternatives, but the absence of sibling tools reduces the need for such differentiation.

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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