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scrape_avm_module_details

Fetches and extracts key sections from AVM module README.md, including resource types, parameters, and usage examples, by converting GitHub URLs to raw markdown.

Instructions

Fetch and extract specific sections from AVM module README.md, returning formatted markdown.

Extracts:

  • Resource Types

  • Parameters

  • Usage Examples (focusing on large parameter sets)

Converts GitHub URLs like: https://github.com/Azure/bicep-registry-modules/tree/main/avm/res/storage/storage-account

To raw URLs like: https://raw.githubusercontent.com/Azure/bicep-registry-modules/refs/heads/main/avm/res/storage/storage-account/README.md

Args: url (str): AVM GitHub repository URL

Returns: str: Formatted markdown string containing the extracted sections

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations provided, so description carries full burden. It explains the URL conversion and extraction but does not disclose potential issues like rate limits, authentication requirements, error handling for invalid URLs, or size of output.

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 front-loaded with the main purpose, uses bullet points for clarity, and includes a concrete example. No superfluous sentences.

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 output schema exists, the description need not explain return values, but it does describe extracted sections. It covers main functionality, but could mention behavior on invalid URLs or missing modules.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'url' is thoroughly explained: its purpose as AVM GitHub repository URL, with an example showing the transformation to raw URL. Schema description coverage is 0%, so the description compensates well.

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 fetches and extracts specific sections from AVM module README.md and returns formatted markdown. It lists the extracted sections and distinguishes from sibling tool list_avm_modules, which lists modules.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains what the tool does and mentions URL conversion, but does not explicitly state when to use it vs alternatives or when not to use it. The sibling tool is named but not compared.

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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