Ultra-fast web scraper and deep discussion crawler delivering clean Markdown for AI Agents
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.
Average 4.1/5 across 1 of 1 tools scored.
There is only one tool, so there is no possibility of an agent selecting the wrong tool from a set. The tool's description clearly explains its input-based modes, eliminating internal ambiguity about what it does.
With a single tool named 'maxcrawl' matching the server name, there are no inconsistent naming conventions across tools. While the name is not verb_noun, consistency is trivially satisfied.
A single tool feels thin for a server that advertises both web crawling and deep research, but the unified input-detection design intentionally consolidates multiple workflows into one entry point. This is borderline, not egregiously under-scoped.
The tool covers topic-based research, single URL extraction, concurrent multi-URL extraction, and recursive domain crawling, which covers the core stated purpose. It lacks granular controls or status/reporting, but these are not major gaps for a unified single-tool design.
maxcrawlAInspectMaxCrawl unified smart web crawler & deep research tool. Automatically detects input type: (1) If given a topic/query -> searches & performs multi-source research; (2) If given a single URL -> extracts clean markdown; (3) If given multiple URLs -> extracts all concurrently; (4) If given a domain with depth > 1 -> runs recursive site crawl.
| Name | Required | Description | Default |
|---|---|---|---|
| urls | No | Optional array of URLs for batch extraction | |
| depth | No | Crawl depth for recursive crawling (default: 1) | |
| target | No | Target URL (e.g. "https://..."), search keyword/topic (e.g. "Cursor vs Windsurf 2026"), or list of URLs | |
| maxPages | No | Maximum number of pages or search results to crawl |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It does disclose the auto-detection logic and major behaviors (search, markdown extraction, concurrency, recursion). However, it does not mention potential side effects, rate limits, expected runtime, or what 'multi-source research' returns, leaving some behavioral ambiguity.
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 compact, front-loaded, and uses a numbered list to efficiently present four distinct modes. Every sentence contributes useful information; there is no filler or repetition of schema details.
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 no output schema and no annotations, the description covers invocation logic well but omits return-value expectations and edge behaviors (e.g., what happens if depth=1 on a domain, or what 'multi-source research' returns). While the four modes are clear, the lack of output/error/edge-case details makes it not fully complete for a multi-modal tool.
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%, so the baseline is 3. The description adds meaningful semantic context beyond the schema by explaining how multiple parameters interact—e.g., 'domain with depth > 1' triggers recursive crawling, and 'target' can be a query, single URL, or list. This clarifies parameter intent beyond the bare property names.
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 identifies this as a web crawler/deep research tool and enumerates four concrete input modes: topic/query research, single-URL markdown extraction, concurrent multi-URL extraction, and recursive domain crawling. It goes beyond the tool name by specifying the exact resources and actions involved.
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?
It provides clear usage context by mapping input types to behaviors ('If given a topic/query -> searches...', 'If given a single URL -> extracts clean markdown', etc.). There are no explicit exclusions or alternative tools mentioned, but since there are no siblings, this level of guidance is effective.
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.
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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.
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