surface_markdown_parserExecutes fetch on any public URL, strips scripts/ads/nav noise, and returns compressed semantic Markdown optimized for LLM token ingestion. Price: $0.002 USDC per call.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Public http(s) URL to fetch and convert |
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?
With no annotations, the description bears the full burden. It transparently describes the fetch, noise removal, compression, and LLM optimization, and includes pricing. However, it does not mention error handling, side effects, or rate limits, which would improve transparency.
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 extremely concise with two meaningful sentences. The first sentence explains the core action and output, and the second provides pricing. No superfluous information is included.
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 simple tool with one parameter and no output schema, the description covers the main functionality. However, it lacks differentiation from the sibling and does not describe error behavior or output format details, making it adequate but not comprehensive.
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 100% with a single 'url' parameter described as 'Public http(s) URL to fetch and convert'. The tool description adds no further detail about the parameter beyond what the schema provides, so it meets the baseline of 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 the tool fetches any public URL and returns cleaned Markdown, with specific verbs and resource. However, it does not explicitly distinguish from the sibling 'stealth_markdown_parser', leaving some ambiguity about when to use each.
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?
No guidance is provided on when to use this tool versus the sibling 'stealth_markdown_parser' or any alternative. The description only states what the tool does without usage context.
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.
Both tools parse web pages to Markdown, but they target different use cases: stealth handles JavaScript-heavy/SPA pages with a headless browser, while surface provides a basic fetch with noise stripping. Their distinct capabilities and price points make them easily distinguishable.
Both tool names follow a consistent pattern: an adjective (stealth_/surface_) followed by 'markdown_parser'. They share the same verb_noun structure and use underscores consistently.
With only two tools, the server feels minimal. While the two tools cover two main tiers of web parsing, the count is below the typical 3-15 range for a well-scoped server, but still reasonable for a focused purpose.
The tool set covers the primary use cases for converting web pages to Markdown: a basic mode for simple pages and a stealth mode for complex ones. Minor gaps exist, such as no batch processing or error handling tools, but the core functionality is well-covered.