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quanticdata-mcp

by io.github.quantumproxies

search_and_read

Read-only

Search the live web, fetch the top organic pages as clean Markdown, and return citation-ready numbered sources plus one token-bounded context string ready for an AI prompt. Use this when the goal is answering/researching, and use search when raw SERP structure or a specialized vertical is needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoSearch UI language, e.g. 'en' or 'it'
queryYesThe research/search query
top_nNoTop organic pages to fetch (default 3, max 5)
engineNoSearch engine (default google)
countryNoISO country code for search and proxy geo
max_tokensNoMaximum estimated tokens in the assembled context (default 8000)
fetch_contentNoFalse returns snippet-only context without fetching result pages

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already mark readOnlyHint=true and openWorldHint=true. The description adds meaningful behavioral context: live-web freshness, Markdown conversion, numbered citations, and token-bounded context assembly. It doesn't disclose all edge behaviors (e.g., fetch failures or citation formatting nuances), but for a read-only search tool this is sufficient and does not contradict 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?

The description is two sentences, front-loaded with the core operation and output shape, followed by the usage distinction. Every sentence earns its place, and there is no redundant fluff.

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?

With no output schema, the description clearly explains what the caller receives: numbered sources and one token-bounded context string. It could add more detail on the exact format or behavior on sparse results, but the fully documented input schema, read-only annotation, and precise sibling comparison make this definition largely complete.

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 the input schema already documents all seven parameters with defaults, ranges, and enums. The description indirectly reinforces top_n and max_tokens through 'top organic pages' and 'token-bounded context,' but it does not add substantial parameter-level meaning beyond what the schema provides.

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 and resource: 'Search the live web, fetch the top organic pages as clean Markdown, and return citation-ready numbered sources plus one token-bounded context string.' It clearly distinguishes itself from the sibling 'search' by contrasting raw SERP needs, so an agent can tell them apart immediately.

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?

It explicitly states when to use the tool: 'Use this when the goal is answering/researching.' It also gives a clear exclusion: 'use `search` when raw SERP structure or a specialized vertical is needed.' This gives the agent both positive and negative usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All quanticdata-mcp toolsInspect
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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