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Tech Stack Change Detector

tech-stack-change-detector
Read-only

Detect a website's current technologies (CMS, ecommerce, analytics, marketing/CRM, framework, hosting/CDN, chat, payments) and diff it against a previously-seen stack you supply, so you get exactly what was added or removed. No browser, no proxies, no login. — $0.01/call, x402 (USDC on base).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYesOne entry per domain. Bare form: "example.com" (just detects the current stack). Diff form: "example.com|Tech1,Tech2,Tech3" — everything after the pipe is the stack you last saw for this domain; the Actor returns what was added/removed vs. right now.
maxConcurrencyNoHow many domains to check in parallel.

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already include readOnlyHint=true and destructiveHint=false. The description adds valuable context beyond these: 'No browser, no proxies, no login' (direct HTTP), pricing of $0.01/call paid via x402 (USDC on base), and the diff semantics that rely on a user-supplied previous stack.

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?

Two concise sentences deliver the core function, behavioral constraints, and cost. The description is front-loaded with the main purpose and every sentence provides distinct value with no redundancy.

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?

With only 2 parameters, no nested objects, and a detailed schema, the description is sufficiently complete. It conceptually covers the return value ('exactly what was added or removed'), though with no output schema, a bit more detail on the response format could be beneficial, but it is not critical.

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?

The input schema descriptions cover both parameters fully, including the pipe-separated syntax for items and the default/max for maxConcurrency. The tool description only restates the diff concept without adding parameter-level detail, so it stays at the 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 uses specific verbs and resources: 'Detect a website's current technologies' and 'diff it against a previously-seen stack you supply'. It enumerates tech categories (CMS, ecommerce, analytics, etc.) and clearly distinguishes from the sibling tech-stack-detector by the diff capability.

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?

The description explains that diffing requires a previously-seen stack supplied by the user, and indicates that bare domains just detect the current stack. However, it does not explicitly name alternatives like tech-stack-detector for when no previous stack exists, so the guidance is clear but not exhaustive.

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