Parallect MCP Server
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- FlicenseAqualityNot gradedmaintenanceEnables AI assistants to perform comprehensive research by searching Google, mining Reddit discussions, scraping web content with JS rendering, and synthesizing findings with citations into structured context.5165 npm3-
- FlicenseNot gradedqualityBmaintenanceEnables AI agents to perform deep web research by searching, fetching, chunking, retrieving, and synthesizing cited answers from exact source passages via a deep_research tool.-
- AlicenseAqualityDmaintenanceEnables AI agents to run research queries across multiple public sources (Hacker News, Reddit, GitHub, Brave Search) in parallel, returning normalized results.5MIT
- AlicenseNot gradedqualityNot gradedmaintenanceConnects AI assistants to the Maestro research framework to orchestrate multi-agent research missions, including planning, research, and writing phases. It enables users to launch research tasks, track real-time progress, and retrieve comprehensive structured reports and notes.-
- AlicenseNot gradedqualityCmaintenanceEnables users to run autonomous multi-stage deep research directly from MCP-compatible clients, producing cited, publication-quality technical reports from web searches and unstructured data.1MIT
- AlicenseAqualityDmaintenanceEnables AI agents to perform professional-grade deep research by aggregating real-time data from multiple sources, evaluating source credibility, and generating comprehensive reports.311Apache 2.0
TDQS
Scored across 11 tools
Tools target distinct stages of the research lifecycle and the descriptions explicitly resolve the riskiest overlap (research creates a new thread vs follow_up continues an existing one; research_status vs get_results distinguish progress from output). The only mild overlap is usage (spend analytics) versus balance (credit balance), but both descriptions make the boundary clear.
All names are snake_case, but verb styles are mixed: some are verb_noun (get_results, list_threads, search_claims, get_claim_evidence), some are bare nouns (research, usage, balance), and one is noun_status (research_status). It remains readable, but the pattern is not predictable.
Eleven tools sit squarely in the well-scoped 3-15 range. Each tool maps to a distinct capability (submit, poll, retrieve, follow up, browse threads, inspect claims, list providers, view spend/balance) with no filler.
The surface covers the full research lifecycle: submission, status polling, result retrieval, follow-ups, thread history, claim/evidence inspection, provider listing, and usage/billing. Minor gaps exist (no explicit cancel-job or delete-thread operation), but agents can work around these.