1cent Web Intelligence for AI Agents

URL Changed

web.url.changed

Compare a public HTTP or HTTPS URL with its previously stored normalized content hash. Creates a baseline on first use, then reports whether content changed and returns current and previous hashes with timestamps. JavaScript is not executed. Pass url as an absolute public HTTP(S) URL. Keep fresh=false to reuse cache; set fresh=true only for a new fetch.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`url`	Yes	Absolute public HTTP or HTTPS URL to inspect. Private, loopback, link-local, metadata-service and otherwise SSRF-sensitive destinations are rejected.
`fresh`	No	Set true only when a new upstream fetch is required; false allows the bounded cached result and is cheaper for the origin.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`changed`	Yes
`quality`	No
`checked_at`	Yes
`current_hash`	Yes
`first_seen_at`	No
`previous_hash`	Yes
`baseline_created`	Yes
`previous_checked_at`	No

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

AddedJul 30, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Given annotations (readOnlyHint=false, openWorldHint=true, idempotentHint=false), the description adds significant value by explaining the baseline creation, caching behavior, and that JS is not executed. It thoroughly discloses side effects and state changes.

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 with no fluff: first sentence defines purpose, second clarifies JS and url requirements, third gives caching advice. Every sentence earns its place and is front-loaded with key information.

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 an output schema present, return values are covered. The description covers main functionality, parameter usage, and behavioral notes. Minor gaps include not mentioning SSRF rejection or error cases, but these are in the schema.

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% with descriptions for both parameters. The description reinforces the schema by clarifying url requirements and the caching role of fresh. It provides contextual guidance beyond what the schema alone offers.

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 it compares a URL with a stored hash to detect changes, creates a baseline on first use, and returns hashes with timestamps. This specific verb+resource distinguishes it from sibling tools like web.url.hash or web.url.diff.

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 when to use fresh=false (reuse cache) and fresh=true (new fetch), and notes that JavaScript is not executed. It lacks explicit comparison to alternatives but provides clear parameter guidance.

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

All 1cent Web Intelligence for AI Agents tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.4/5.0

Disambiguation4/5

Most tools have distinct purposes, but some overlap exists among text extraction tools (url_extract, url_text, url_markdown, url_rag_chunks). Descriptions help differentiate them, so disambiguation is mostly clear.

Naming Consistency5/5

All tools follow a consistent prefix (catalog_, demo_, site_, url_) and use lowercase snake_case with descriptive names. Conventions are uniform throughout.

Tool Count5/5

35 tools cover a comprehensive range of URL and site analysis functions without feeling bloated. Each tool serves a specific purpose, and the count is appropriate for the server's scope.

Completeness5/5

The tool set covers all major aspects of URL analysis: health, content, metadata, change detection, site discovery, and security. No obvious gaps for the stated domain.

Resources

Need Help?