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Social Preview Checker

social-preview-checker
Read-only

See exactly how your links look when shared on Telegram, WhatsApp, X, LinkedIn, Slack, Facebook & Discord. Bulk-audit Open Graph and Twitter Card tags, validate preview images, and find what breaks your link previews. — $0.01/call, x402 (USDC on base).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYesList of page URLs to check (e.g. `example.com/article` or a full https URL).
maxConcurrencyNoHow many URLs 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 declare readOnlyHint=true and destructiveHint=false, so the description builds on that by adding cost details ($0.01/call, x402) and specific behaviors like validating images and diagnosing broken previews. This enriches the agent's understanding of what the tool actually does during a read-only operation, going beyond the safety profile.

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?

The description is concise and front-loaded, with each sentence providing essential information: what the tool does, what it audits, and its pricing. There is no redundant or filler content, making it efficient for an agent to parse quickly.

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?

Despite lacking an output schema, the description gives a clear sense of the tool's capabilities and scope, including platforms, tag types, and pricing. It doesn't explicitly describe the return format or edge cases, but the simplicity of the tool and strong parameter coverage make this a minor gap, warranting a 4.

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%, so both parameters (urls and maxConcurrency) are already fully documented in the schema. The description adds no additional parameter-level detail beyond the schema's existing explanations, meeting the baseline of 3 without enhancing semantics.

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's function with specific verbs: 'See exactly how your links look', 'Bulk-audit Open Graph and Twitter Card tags', 'validate preview images', and 'find what breaks your link previews'. It names specific platforms (Telegram, WhatsApp, X, etc.) and uniquely identifies the resource being checked (link previews), distinguishing it from sibling tools.

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 provides clear context for when to use the tool—when sharing links on social platforms and wanting to check previews. It implicitly conveys the use case without explicitly listing alternatives or exclusions. The presence of platforms and audit activities makes the intended scenario obvious, though no direct 'when not to use' guidance is given.

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

A4/5.0
Disambiguation2/5

Several tools have overlapping purposes: ai-crawler-access-checker and llms-txt-auditor both check robots.txt for AI crawler access, while ai-answer-change-alert, ai-overview-tracker, and llm-brand-visibility all track AI-generated citations/visibility. An agent may struggle to pick the right one without reading full descriptions.

Naming Consistency4/5

Most tools use lowercase hyphenated noun phrases (e.g., ai-answer-change-alert, social-preview-checker), but pricing_info uses an underscore, breaking the otherwise consistent pattern. Minor deviation but still readable.

Tool Count5/5

Seven tools is well-scoped for a specialized SEO/AI-visibility server. Each tool addresses a distinct aspect (crawler access, llms.txt, AI citations, brand visibility, social previews) without being excessive, and the pricing_info helper is a sensible addition.

Completeness4/5

The set covers the core AI-era SEO workflow: audit AI crawler access, validate llms.txt, track AI overview citations, monitor answer changes, and check brand visibility. Missing obvious features like generating llms.txt or deeper competitor analysis, but no critical dead ends.

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