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quanticdata-mcp

by io.github.quantumproxies

seo_audit

Read-only

Audit a URL's SEO in one call: fetches it twice — as a pure HTTP bot (no JS) and fully rendered — and returns both views (title, description, canonical, h1, word count) plus the diff (JS-only content, changed title/description, canonical missing without JS) and bot-facing meta (robots, Open Graph, JSON-LD types). Use this instead of scraping manually when checking how a page indexes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe page URL to audit
countryNoISO country code for the proxy exit, e.g. 'us'
no_renderNoSkip the rendered pass (cheaper — returns the no-JS view only, no diff)

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

The description discloses the dual-fetch behavior (pure HTTP and fully rendered), the exact return elements (title, description, canonical, h1, word count, diff, bot-facing meta), and the effect of the no_render parameter. This goes well beyond the readOnlyHint annotation and gives a precise mental model.

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?

The description is succinct but information-dense. It leads with the core purpose, then lists the outputs and diff, and ends with a usage hint. It avoids redundancy and each sentence adds value, though it could be slightly more streamlined by omitting parentheticals.

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?

Given there is no output schema, the description thoroughly covers what the tool returns (both views and diff) and the filtering options. It explains the dual-fetch logic and the bot-facing meta, providing enough context for an agent to decide when and how to call it. No critical aspects are missing.

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 schema already fully documents all three parameters with descriptions (url, country, no_render). The tool description adds no additional meaning—e.g., it does not clarify the purpose of the country parameter or explain default behavior beyond what the schema states. Since coverage is 100%, baseline is 3 and no extra value is added.

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 audits a URL's SEO in one call, specifying the resource (URL) and the action (audit). It distinguishes from manual scraping by offering a comprehensive alternative, making the purpose immediately obvious.

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?

It explicitly recommends using this tool instead of manual scraping when checking how a page indexes, providing a clear scenario. However, it does not compare against other sibling tools like 'scrape' or 'crawl', leaving some ambiguity about when to prefer this over those.

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

A4.1/5.0
Disambiguation5/5

Each tool targets a distinct resource or action: single scrape, batch scrape, crawl, search, dataset creation, parser lifecycle, proxy management, and SEO audit. Even the five status pollers are clearly differentiated by job type and their descriptions explicitly state which job they poll, so an agent can reliably select the right tool.

Naming Consistency4/5

Most names follow a verb-first pattern (create_dataset, generate_parser, run_collector, save_parser_preset, whitelist_ip) and listing tools consistently use the 'list_' prefix. However, a few are noun-first (parser_preset_stats, proxy_locations, collector_run_status) and the status polling tool for collectors breaks the otherwise consistent '<job>_status' convention ('collector_run_status' instead of 'run_collector_status').

Tool Count3/5

At 25 tools, the set is at the upper edge of the 'heavy' range. The tools all serve distinct functions, reflecting a broad platform covering scraping, crawling, search, datasets, parsers, proxies, and SEO, but the count borders on overwhelming for an agent, and some consolidation (e.g., a generic async job status endpoint) could reduce the surface.

Completeness4/5

The tool surface covers the core data-extraction lifecycle well: discovery (map, search), acquisition (scrape, batch, crawl), structured extraction (generate_parser, save_parser_preset, parser stats/heal), proxy management, and result aggregation (datasets, collectors). Notable gaps are the absence of any cancellation/abort mechanism for long-running async jobs and no way to delete a parser preset, but these are minor for most workflows.

Resources