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rcarmo

office-document-mcp-server

by rcarmo

web_fetch

Read-only

Fetch any web page and extract its main content as clean Markdown, removing navigation and ads.

Instructions

Fetch a web page and extract its content as Markdown.

Uses readability to extract the main content from the page, removing navigation, ads, and other clutter. Then converts the clean HTML to Markdown format.

Example: web_fetch(url="https://example.com/article")

web_fetch(
    url="https://docs.microsoft.com/en-us/azure/...",
    include_links=True,
    include_images=True
)

Args: url: The URL to fetch extract_content: Use readability to extract main content (default: True) include_links: Include hyperlinks in output (default: True) include_images: Include image references (default: False) timeout: Request timeout in seconds (default: 30)

Returns: Dictionary with title, content (markdown), url, and metadata

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe URL to fetch
extract_contentNoUse readability to extract main content (default: True)
include_linksNoInclude hyperlinks in output (default: True)
include_imagesNoInclude image references (default: False)
timeoutNoRequest timeout in seconds (default: 30)
Behavior5/5

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

Adds substantial detail beyond annotations: uses readability to remove clutter, converts to Markdown, and explains parameter defaults. 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?

Well-structured: brief introduction, two examples, and organized Args/Returns sections. Every sentence provides useful information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, description fully explains return values (title, content, url, metadata). Covers all necessary details for agent use.

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 coverage is 100%, baseline 3. However, description provides additional context for each parameter in the Args block and examples, adding value.

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?

Clearly states it fetches a web page and extracts main content as Markdown using readability. Distinguishes from siblings like web_search, web_extract_links, web_extract_tables.

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?

Provides clear context via examples and description of content extraction. Does not explicitly say when not to use, but sibling tools cover alternatives.

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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