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Web Intel

by com.timzinin.api

Website Tech Stack Detector

tech-stack-detector
Read-only

Detect the technologies a website runs — CMS, ecommerce platform, analytics, marketing/CRM, JS framework, hosting/CDN, chat and payments — straight from its public HTML and HTTP headers. No browser, no proxies, no login. Built for sales targeting and competitive research. — $0.01/call, x402 (USDC on base).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
websitesYesList of websites to fingerprint (e.g. `shopify.com` or `https://example.com`). One row per site.
maxConcurrencyNoHow many websites to fingerprint in parallel.

TDQS

A4.1/5.0
Behavior5/5

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

Adds valuable behavioral context beyond annotations: 'No browser, no proxies, no login' clarifies operational mode, and '$0.01/call' discloses the cost model. Annotations already declare read-only, and the description is consistent with them.

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?

Three sentences efficiently cover the core function, operational traits, use case, and pricing. Each sentence adds distinct value, and the main purpose is front-loaded.

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

Completeness4/5

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

Lists the expected detection categories (CMS, ecommerce, analytics, etc.), giving a good sense of the output. However, no output schema exists, and the description does not specify the exact return format or whether results include confidence scores, leaving a minor gap.

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

Parameters3/5

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

Schema description coverage is 100%, with both 'websites' and 'maxConcurrency' fully described in the schema. The description adds no extra parameter semantics beyond what the schema already provides, so baseline 3 is appropriate.

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

Purpose4/5

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

Clearly describes the tool as detecting website technologies (CMS, ecommerce, analytics, etc.) from HTML and HTTP headers. However, it does not explicitly differentiate from the sibling 'tech-stack-change-detector', which likely provides change tracking over time.

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?

States 'Built for sales targeting and competitive research', giving a clear use context. No explicit exclusions or alternatives are mentioned, but the use case is specific enough to guide selection.

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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TDQS

A3.9/5.0
Disambiguation2/5

There are multiple overlapping tool pairs: tech-stack-detector vs tech-stack-change-detector (the latter is a superset that includes detection), and url-to-markdown vs structured-extract vs sitemap-to-knowledge (all fetch web pages and convert content, differing only in output format). These boundaries are unclear, and an agent could easily select the wrong one without deep reading of descriptions.

Naming Consistency2/5

Naming is a mix of hyphenated descriptors (domain-health-checker, tech-stack-detector), snake_case (pricing_info), and verb phrases (structured-extract, url-to-markdown). The pattern is inconsistent: some tools are named after the action (extract, convert), others after the target (shopify-store-intelligence). This makes it hard to predict tool names.

Tool Count5/5

With 10 tools, the count is well within the 3-15 ideal range. Each tool addresses a distinct web intelligence need (domain health, tech stack, e-commerce, content extraction, pricing), and none are purely redundant filler. The scale feels appropriate for the server's stated purpose.

Completeness4/5

The surface covers core web intelligence workflows well: domain auditing, tech stack detection (with change detection), content extraction, and e-commerce monitoring for Shopify and Zid. Minor gaps exist, such as missing generic e-commerce platform coverage or a dedicated WHOIS lookup, but these are not critical given the existing domain-health-checker.

Resources