Skip to main content
Glama
nymrel
by nymrel

nymrel_crawler_mesh

Crawl web pages or raw HTML to extract clean, token-optimized Markdown, removing boilerplate, ads, scripts, and navigation for LLM-ready input.

Instructions

High-throughput clean web crawler & Markdown AST extractor. Strips boilerplate, ads, scripts, and navigation to produce token-optimized Markdown for LLMs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoTarget URL to crawl and scrape
htmlNoDirect HTML string payload to convert to Markdown AST
extractMetadataNoWhether to extract OpenGraph, Twitter, and JSON-LD metadata
Behavior3/5

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

Annotations are absent, so the description must disclose behavior alone. It does state that it strips boilerplate, ads, scripts, and navigation, and produces token-optimized Markdown, which gives a clear picture of the transformation. However, it does not mention the ability to take direct HTML input (via the 'html' parameter) or the metadata extraction behavior (via 'extractMetadata'), leaving some behavioral aspects undisclosed. It also does not mention any side effects or safety implications.

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?

A single, front-loaded sentence that packs a lot of information without fluff. It begins with 'High-throughput' setting expectations, then clearly identifies the tool as a crawler and extractor, and ends with the output format. Every word earns its place.

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?

For a tool with no annotations and no output schema, the description provides the essential purpose and output format (token-optimized Markdown for LLMs). It explains the cleaning behavior, but omits details about the dual input modes (URL vs HTML) and the metadata extraction flag, which are covered in the schema. Given the schema already documents those, the description is reasonably complete, though it could mention the read-only nature and the fact that it returns extracted content.

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 schema provides 100% coverage with descriptions for all three parameters (url, html, extractMetadata). The description adds context about the cleaning and token-optimization purpose, but does not individually explain parameters beyond what the schema already says. Since schema coverage is high, this earns the baseline 3.

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 it is a web crawler and Markdown AST extractor, with specific verbs 'crawl' and 'extract'. It mentions stripping boilerplate, ads, scripts, and navigation, which distinguishes it from typical simple fetchers, and the mention of token-optimized output for LLMs makes its intent clear. It is not a tautology and stands apart from sibling tools.

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?

The description provides no guidance on when to use this tool versus alternatives. It does not mention any conditions, prerequisites, or exclusions. Sibling tools have distinct names but no comparative advice is given, so an agent has no basis to choose this over others besides the name.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/nymrel/nymrel-mcp-hub'

If you have feedback or need assistance with the MCP directory API, please join our Discord server