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quanticdata-mcp

by io.github.quantumproxies

map

Read-only

Discover a site's URLs fast (robots.txt sitemaps + /sitemap.xml + homepage links) without a full crawl. Returns up to limit URLs (default 100) plus the site-wide total and a per-section summary (e.g. '/blog': 1988) so you see the site's shape without the full list. Narrow with search (substring filter — the primary way to find specific pages) or set group_by 'path' for the path tree with counts instead of URLs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe site URL to map
limitNoMax URLs returned (default 100). `total`/`summary` always cover the whole site.
searchNoOnly return URLs containing this substring — use this to narrow before raising limit
group_byNopath: return the path tree with per-prefix counts instead of the flat URL list
includeSubdomainsNoInclude subdomains of the seed host

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already mark readOnlyHint and openWorldHint as true, and the description adds valuable behavioral context: the URL discovery sources, default limit, return of `total` and `summary`, and the `group_by` path-tree mode. It goes beyond annotations by describing the output shape and narrowing semantics.

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 front-loaded with the core purpose and uses three focused sentences. It packs return-shape details and parameter guidance without redundancy, though it is slightly dense in the middle.

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?

Given no output schema, the description adequately explains what the tool returns (limit, total, summary, path tree). It covers the main behaviors and parameter semantics, but does not mention any rate limits or auth needs; for a read-only mapping tool this is a minor gap.

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 the baseline is 3. The description adds meaningful semantic detail beyond the schema, particularly for `search` ('substring filter — the primary way to find specific pages') and `group_by` ('path tree with counts instead of URLs').

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: 'Discover a site's URLs fast' using robots.txt/sitemaps/homepage links. Explicitly differentiates from a full crawl, which distinguishes it from the sibling crawl tool.

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?

Offers clear guidance: use map for fast URL discovery without a full crawl. It also explains when to use the `search` parameter as the primary way to find specific pages, giving the agent actionable routing criteria.

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