NodeProxy Web Surface Markdown Parser
Server Details
x402 MCP web parser: URLs to Markdown for agents. USDC on Base, Polygon, Arbitrum, Ethereum.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.6/5 across 2 of 2 tools scored.
Both tools parse web pages to Markdown, but one targets JavaScript-heavy/SPA pages with stealth, while the other handles simpler pages cheaply. Their purposes are clearly distinct with no overlap.
Both tool names follow a consistent adjective_markdown_parser pattern, making them predictable and easy to differentiate.
Two tools is slightly below the typical 3-15 range, but acceptable for a focused service. However, more tools could be added for additional features.
The tool surface covers basic parsing needs for two scenarios, but lacks features like custom extraction options, error handling, or batch processing, leaving notable gaps for advanced use.
Available Tools
2 toolsstealth_markdown_parserStealth Anti-Bot Markdown ParserAInspect
Hardened headless-browser fetch with full JavaScript/SPA rendering and a realistic browser profile, returning fully rendered Markdown. Best for JavaScript-heavy/SPA pages and light bot checks; not guaranteed against advanced anti-bot walls (e.g. Cloudflare/Akamai). Price: $0.05 USDC per call.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Protected http(s) URL to fetch via stealth pipeline |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses the tool's stealth nature, headless browser usage, JavaScript rendering, browser profile, limitation against advanced anti-bot walls, and cost. All behavioral traits are transparent.
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?
Three sentences with no wasted words. Front-loaded with purpose, then usage, limitations, and cost. Highly efficient.
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 one parameter, no output schema, and clear purpose, the description fully covers what the tool does, when to use it, its limitations, and cost. No gaps.
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% for the single 'url' parameter, which is described as 'Protected http(s) URL to fetch via stealth pipeline'. The description adds no further parameter-specific meaning beyond the schema, so 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?
Description clearly states it fetches a URL via a stealth headless browser and returns fully rendered Markdown. It distinguishes from the sibling 'surface_markdown_parser' by specifying it's for JavaScript-heavy/SPA pages and light bot checks.
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?
Explicitly states when to use: 'Best for JavaScript-heavy/SPA pages and light bot checks', and when not: 'not guaranteed against advanced anti-bot walls' (e.g., Cloudflare/Akamai). Provides clear context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
surface_markdown_parserWeb Surface Markdown ParserAInspect
Executes 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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description takes full burden. Describes fetching, stripping of scripts/ads/nav noise, and compression to Markdown. Also discloses cost. Missing details on failure modes or rate limits, but core behavior is clear.
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, highly efficient. First sentence delivers main purpose and process, second sentence adds cost. No redundant wording.
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?
Tool is simple with one parameter and no output schema. Description covers input, processing steps, and output format. Could mention error handling or size limits, but for a straightforward web parser, it 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 coverage is 100% with a description for the URL parameter. The description adds that the URL must be public, which is additional semantics beyond the schema's format constraint.
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?
Clearly states the verb (fetches), resource (public URL), and result (compressed semantic Markdown). It is specific and distinguishes from any potential siblings by describing the transformation process.
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?
Specifies that the URL must be public and that the output is for LLM token ingestion, implying use cases. Also mentions pricing, which is a usage constraint. However, it does not explicitly state when not to use or provide alternatives, but given no siblings, this is adequate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!