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tech_stack

Analyze a website's HTTP headers, meta tags, and JavaScript fingerprints to detect its CMS, framework, CDN, analytics, and more for competitive research or sales prospecting.

Instructions

Detect the technology stack powering a website.

Analyses HTTP headers, HTML meta tags, JavaScript fingerprints, and
resource paths to identify the CMS, frontend framework, CDN, analytics
platform, e-commerce engine, and other technologies in use.

Useful for competitive research, sales prospecting, migration planning,
or understanding what stack a client is running before proposing solutions.

Args:
    url: Full URL of the site to analyse (must include https://).
        The homepage is usually sufficient; the scan does not crawl.

Returns:
    technologies: List of detected technologies, each with:
        name: Technology name (e.g. 'WordPress', 'Cloudflare', 'React').
        category: Category (CMS, CDN, Analytics, Framework, etc.).
        confidence: Detection confidence 0.0–1.0.
        version: Version string if detectable (may be null).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
Behavior4/5

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

Describes methods (HTTP headers, meta tags, JS fingerprints, resource paths) and notes the scan does not crawl, only the homepage is needed. No annotations are provided, so the description carries the full burden; it adequately discloses behavior without contradictions.

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

Conciseness5/5

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

Well-structured with a clear purpose sentence, followed by method details, use cases, and a docstring for parameters and return values. Every sentence adds value without unnecessary verbosity.

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?

For a tool with one parameter and no output schema, the description fully explains input requirements and return structure (list of technologies with name, category, confidence, version). It is complete and self-contained.

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

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite the schema having 0% description coverage, the description includes a detailed parameter doc: 'Full URL of the site to analyse (must include https://). The homepage is usually sufficient; the scan does not crawl.' This adds essential meaning beyond the schema.

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 'Detect the technology stack powering a website', specifying the verb 'Detect' and resource 'technology stack'. It distinguishes from sibling tools (e.g., dns_lookup, ssl_check) by focusing on stack identification.

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

Usage Guidelines4/5

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

Explicitly lists use cases (competitive research, sales prospecting, migration planning) and notes the tool is for understanding a client's stack. It does not directly state when not to use, but given sibling context, the intended usage is clear.

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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