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webability

by io.github.snayyar00
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Detect the site framework

detect_framework
Read-onlyIdempotent

Detect a page's stack as two separate fields: framework — the application framework, CMS or site builder (e.g. nextjs, nuxt, sveltekit, vitepress, astro, gatsby, wordpress, shopify, mediawiki, vue, react; "unknown" when no signal) — and cssToolkit (tailwind, bootstrap, mui, plain-css). Lists the evidence it used and the value to pass as generate_ai_fix framework. scan_page reports the same two fields from the same detection. NOTE: on this HOSTED server, localhost and private addresses are refused — it runs in our cloud and cannot reach your machine. Two ways to scan a local dev server: run the MCP locally (npx -y @webability/mcp, simplest — nothing leaves the machine), or open a tunnel (webability-tunnel --port 3000) and pass its URL as url together with the printed secret as tunnel_secret.

Input Schema

NameRequiredDescriptionDefault
urlYesURL to inspect
contextNoExplain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
llm_modelNoThe exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
tunnel_secretNoSecret printed by `webability-tunnel`. Required when `url` is a tunnel URL; the URL alone will be refused by the relay. Ignored otherwise.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • removedInput schema / properties / conversation_id
      Removed value: -{
-  "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.",
-  "type": "string"
-}
  2. Changed1 schema field changed
    • changedInput schema / required
      Previous value: -[
-  "url",
-  "context",
-  "llm_model"
-]New value: +[
+  "url"
+]
  3. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare the safe read profile, but the description adds substantial non-obvious behavior: the hosted server refuses localhost/private addresses, requires `tunnel_secret` for tunnel URLs, and returns an evidence list plus the value to feed into `generate_ai_fix`.

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?

Front-loaded with the core purpose before the network caveats, and each sentence carries information. It is dense and long, with the local-dev-server note consuming significant space, but nothing is pure filler.

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?

With no output schema, the description fully carries the return-value burden by naming both output fields and the evidence list. Combined with the network/tunnel guidance, an agent has everything needed to call it correctly.

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 coverage is 100%, so parameters are already documented, but the description adds real meaning by explaining how `tunnel_secret` is obtained and when it is required, and by tying `framework` output to the `generate_ai_fix` parameter.

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?

States a specific verb and resource ('Detect a page's stack') and enumerates the exact output fields (`framework`, `cssToolkit`) with example values. It also distinguishes itself from the sibling `scan_page`, noting they return the same two fields from the same detection.

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?

Provides clear operating context: it explains that `scan_page` reports the same fields (implying when this narrower tool suffices), and gives explicit remediation paths for local dev servers. It lacks an explicit 'use this instead of X when you only need Y' statement, so it falls short of a 5.

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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