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1cent Web Intelligence for AI Agents

Url Schema Validation

web.url.schema_validation
Read-onlyIdempotent

Validate embedded JSON-LD syntax and required Schema.org identifiers. Use only for public HTTP(S) resources; it does not execute JavaScript or bypass access controls. Pass url as an absolute public HTTP(S) URL. Keep fresh=false to allow cache reuse; set fresh=true only when a new upstream fetch is required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesAbsolute public HTTP or HTTPS URL to inspect. Private, loopback, link-local, metadata-service and otherwise SSRF-sensitive destinations are rejected.
freshNoSet 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
NameRequiredDescriptionDefault
dataYes
toolYes
qualityNo
url_finalYes
checked_atYes
from_cacheYes
request_idYes
content_hashYes
url_requestedYes

Schema Changelog

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

  1. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context beyond these: the tool does not execute JavaScript, does not bypass access controls, and has a bounded cache mechanism influenced by the fresh parameter. No contradictions exist.

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 cover purpose, constraints, and parameter usage with zero waste. The description is front-loaded with the core action (validate JSON-LD) and then adds necessary caveats about public access and fresh, each sentence earning its place.

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 annotations, full schema descriptions, an output schema present, and only two parameters, the tool is simple. The description covers key operational nuances (SSRF privacy restrictions implied, cache behavior, no JavaScript execution), making it complete for an agent to decide when and how to invoke the tool.

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% for both url and fresh, so the baseline is 3. The description largely mirrors the schema's parameter descriptions (e.g., absolute public URL, fresh cache behavior) without adding substantial new meaning beyond what the schema already provides, so it does not elevate above 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 clearly states the tool validates embedded JSON-LD syntax and required Schema.org identifiers, which is a specific verb and resource. It distinguishes itself from sibling tools like web.url.jsonld (which likely extracts JSON-LD) by emphasizing validation, and the scope is unambiguous.

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

Usage Guidelines5/5

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

Explicit guidance is provided: use only for public HTTP(S) resources, with explicit note that it does not execute JavaScript or bypass access controls. The description also instructs on the fresh parameter (keep fresh=false for cache reuse, fresh=true only when necessary), giving clear context for when and how to use the tool.

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

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