scrape_urlFetches a public HTTP/HTTPS URL, strips boilerplate, and returns the primary content as clean Markdown. Costs $0.005 USDC on Solana via x402, per call — same price as GET /api/v1/scrape.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The public HTTP/HTTPS URL to fetch and convert to Markdown |
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| title | Yes | ||
| markdown | Yes | ||
| word_count | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It adds useful context like the $0.005 USDC cost and the 'public URL' constraint, but it does not disclose behavior around redirects, failures, JavaScript rendering, rate limits, or auth requirements. Basic transparency but not comprehensive.
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?
Two sentences, front-loaded with the primary purpose, followed by pricing info. No redundant phrases; every word earns its place.
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?
The tool is simple (one parameter, output schema present) and the description covers core purpose, URL type, and cost. Minor gaps exist around error handling and edge cases, but given the tool's simplicity, the description is sufficiently complete.
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 description coverage is 100% and the single 'url' parameter is already clearly described. The description adds 'public' and 'clean Markdown' framing but no new syntax or format details beyond what the schema provides. Baseline 3 is appropriate.
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 states a clear verb ('Fetches'), a specific resource ('a public HTTP/HTTPS URL'), and the output ('primary content as clean Markdown'). It implicitly distinguishes from sibling tools (extract_fields, links_url, metadata_url) by focusing on full-page content conversion to Markdown.
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 makes it clear this is for fetching public URLs and getting clean Markdown, which implies its use case. It does not explicitly say when not to use it or mention alternatives (e.g., extract_fields for structured data), but the context is strong enough for basic selection.
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.
Each tool targets a distinct extraction goal: specific fields, links, metadata, or full-page markdown. No overlap in purpose, so an agent can confidently select the right tool.
Naming mixes verb_noun (extract_fields, scrape_url) and noun_url (links_url, metadata_url) patterns. The _url suffix on three tools gives some cohesion, but the inconsistency is noticeable.
Four tools is a compact, focused set for a scraping server. It covers the essential extraction tasks without unnecessary bloat, though a few more specialized tools could be added.
The toolkit covers the core scraping workflow: full content, metadata, links, and targeted field extraction. Missing features like raw HTML or DOM handling are minor gaps, but most typical use cases are addressed.