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123Ergo

unphurl-mcp

check_urls

Analyze multiple URLs simultaneously for security risks, domain intelligence, and technical validation across seven dimensions including redirect behavior, SSL/TLS validity, and brand impersonation detection.

Instructions

Check multiple URLs in a single batch. Returns results for all URLs, handling async processing automatically.

Each URL is analysed across seven dimensions: redirect behaviour, brand impersonation, domain intelligence (age, registrar, expiration, status codes, nameservers via RDAP), SSL/TLS validity, parked domain detection, URL structural analysis, and DNS enrichment. Known and cached URLs return results immediately. Unknown URLs are queued for pipeline processing. This tool automatically polls for results until all URLs are complete or the 5-minute timeout is reached. You don't need to manage polling or job tracking.

If the timeout is reached before all results are complete, returns whatever is available with a clear message indicating which URLs are still processing. The user can check results later via check_history.

Maximum 500 URLs per call. For larger datasets, call this tool multiple times with chunks of up to 500 URLs.

Billing: Same as check_url. Known and cached domains are free. Only unknown domains running through the full pipeline cost 1 credit each. The summary shows pipeline_checks_charged (the actual number of credits consumed). If you don't have enough credits for the unknowns in the batch, the entire batch is rejected with a 402 error telling you exactly how many credits are needed.

Duplicate URLs in the list are automatically deduplicated (processed once, charged once). Invalid URLs get individual error status without rejecting the batch.

Use the "profile" parameter to score all results with custom weights.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYesList of URLs to check (maximum 500 per call)
profileNoName of a custom scoring profile to use for all URLs (optional)
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully discloses behavioral traits: async processing with automatic polling, 5-minute timeout, handling of known/cached vs. unknown URLs, billing details (credits for unknown domains), duplicate deduplication, error handling for invalid URLs, and batch rejection for insufficient credits. No contradictions exist.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded with core functionality, but some sentences could be more concise (e.g., the list of seven dimensions is verbose). Overall, it efficiently covers key aspects without unnecessary fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (batch processing, async, billing) and no annotations or output schema, the description is highly complete. It covers purpose, usage, behavior, parameters, limitations, error handling, and integration with sibling tools, leaving minimal gaps for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

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 value by explaining the 'profile' parameter's purpose ('to score all results with custom weights') and contextual details for 'urls' (max 500, duplicate handling, invalid URL processing), elevating the score above baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Check multiple URLs in a single batch' with specific analysis across seven dimensions (redirect behaviour, brand impersonation, etc.). It distinguishes from sibling tools like check_url (single URL) and check_history (historical results).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidance is provided: use for batch URL checking up to 500 URLs, call multiple times for larger datasets, and use check_history for later results. It also distinguishes from check_url (single URL) and mentions when not to use (insufficient credits leads to rejection).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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