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quanticdata-mcp

by io.github.quantumproxies

create_dataset

Build a structured dataset from a plain-language prompt. Quantic AI plans the search queries, searches Google/Bing/DuckDuckGo, maps the sites it finds and scrapes them into validated rows (CSV/JSON). Returns a job id — poll with dataset_status. Billed per delivered, validated record (email/phone fields cost extra, only when found); the run never exceeds limits.max_cost_usd, and the unspent budget is refunded.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitsNo
promptYesWhat dataset you want, in plain language (e.g. 'coffee roasters in Portland with email and phone')
columnsNoColumns to extract; omit to let the planner infer them
countryNoISO country code for the proxy exit geo
sourcesNoDomain allow/deny lists
webhookNoPublic URL to POST the finished dataset to

Schema Changelog

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

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations only signal readOnlyHint=false and openWorldHint=true, but the description discloses key runtime behavior: it performs live web searches, scrapes external sites, returns an async job id, requires polling via dataset_status, and has a billing model with a hard cost cap and refund. This is substantial behavioral context beyond the annotations.

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?

Four tight sentences: purpose, pipeline, async/polling behavior, and cost model. It is front-loaded with the core purpose and every sentence adds necessary operational information without fluff or repetition.

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?

For a complex async, billed, web-scraping tool with no output schema, the description covers the critical operational points: job id, polling endpoint, cost model, and budget safety. It relies on the input schema for parametric details, which is reasonable given 83% schema coverage, though it does not explain the final dataset delivery format or webhook behavior beyond 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 high (83%), so the baseline is 3, and the description adds extra meaning for the cost-related parameters: email/phone fields are premium and billed only when found, and limits.max_cost_usd is a hard cap with refunds for unspent budget. This clarifies semantics that the bare schema descriptions ('Budget cap for the run') do not fully convey.

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 names a specific verb ('Build') and resource ('structured dataset') and describes the full pipeline: planning queries, searching multiple engines, mapping sites, and scraping validated rows. This clearly distinguishes it from sibling tools like search, scrape, crawl, or map, since it is end-to-end dataset construction from a plain-language prompt.

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 gives clear context: use this tool when you want a structured dataset from a natural-language prompt, and it explains that the tool itself handles search and scraping. However, it does not explicitly name alternatives or state when NOT to use it, leaving some inference to the agent.

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