Convert any URL to clean AI-ready Markdown. Supports webpages, YouTube videos, ArXiv papers, Wikipedia articles, Substack newsletters, RSS feeds, Google Docs, GitHub pages, and more. Returns Markdown with up to 63% fewer tokens than the raw source HTML.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It does reveal key behavioral traits: output is Markdown and it reduces tokens by up to 63%. However, it does not mention potential failure modes (e.g., unsupported URLs, paywalls, authentication), rate limits, or whether content is fetched server-side. This is moderate transparency—enough to understand the core behavior but missing edge-case 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 two sentences long and front-loaded with the core action. The first sentence states the purpose, the second lists supported inputs and a benefit. No redundant details or filler, making it highly concise and well-structured.
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?
For a tool with many supported input types and no annotations, the description lacks essential edge-case coverage: what happens for invalid/unsupported URLs, whether authentication or private content is handled, or any limits on file size/request frequency. While the listed sources and token reduction give a solid overview, the absence of error-handling and constraints leaves gaps. The presence of an output schema reduces the need to explain return values, but behavioral context is incomplete.
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?
Schema coverage is 0% (the only parameter 'url' has no schema description). The description partially compensates by explaining that the URL can be any supported type and by listing examples. However, it does not add specific technical constraints (e.g., URL formats, encoded requirements). Given the single obvious parameter, the minimal added meaning is acceptable, scoring a 3.
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 a specific verb ('Convert'), resource ('any URL'), and outcome ('to clean AI-ready Markdown'). It distinguishes from siblings like convert_arxiv and convert_youtube by positioning itself as the general-purpose converter, listing a wide range of supported sources. This makes the tool's scope immediately clear.
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 provides clear context: use this for any URL, with a broad list of supported types including webpages, YouTube, ArXiv, etc. It does not explicitly mention when to prefer specialized siblings like convert_arxiv, but the 'any URL' framing implies this is the default general tool. There are no exclusions stated, which aligns with a 4 (clear context, no exclusions) rather than a 5 (explicit when/when-not/alternatives).
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.
