Convert multiple URLs to Markdown in a single call.
Maximum 10 URLs per batch. Each URL is converted independently.
Args:
urls: List of URLs to convert. Maximum 10. Each must start with http:// or https://
Returns:
All converted Markdown documents combined, clearly separated with headers
| Name | Required | Description | Default |
|---|---|---|---|
| urls | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses batch behavior, independence of each conversion, URL format constraints, and the return structure. Minor gaps remain around error handling or partial failures, but overall it provides strong behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: a one-sentence overview followed by clear Args and Returns sections. Every sentence adds necessary information without fluff or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a simple one-parameter tool and an output schema, the description covers the key aspects: input constraints, independent processing, and return format. It doesn't discuss error behavior or relationship to siblings in depth, but these are secondary for a straightforward batch converter.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one parameter with 0% coverage, but the description's Args section fully compensates by explaining that 'urls' is a list, max 10, and must start with http/https. This adds meaning beyond the bare schema definition.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Convert multiple URLs to Markdown in a single call,' specifying the verb, resource, and format. This distinguishes it from siblings like convert_url (single URL) and convert_youtube, making the tool's purpose immediately obvious.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for batch conversion with 'multiple URLs' and provides constraints (max 10, URL must start with http/https). However, it doesn't explicitly contrast with alternatives like convert_url or mention when not to use it, falling short of explicit when/when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools overlap: convert_url already supports ArXiv and YouTube, making convert_arxiv and convert_youtube redundant. convert_with_metadata duplicates convert_file/convert_url but adds a metadata header. Agents must read descriptions carefully to choose correctly.
All tool names follow a consistent snake_case verb_noun pattern: convert_*, count_tokens, list_supported_formats, prepare_for_rag. No mixed conventions or vague verbs exist.
9 tools is well-scoped for a document conversion service. Each tool has a distinct role (file, URL, batch, metadata, token counting, format listing, RAG preparation) without being bloated.
The domain of converting sources to Markdown is thoroughly covered: files, URLs, batch conversion, metadata, token counting, and RAG chunking. The only minor redundancy is the specialized source converters, but the core workflow is complete.