research-mcp
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- AlicenseAqualityBmaintenanceAn MCP-native research assistant — search backend, intent-routing, and writing pipeline that carries a question through search, verification, and writing, delivering whatever research you need: quick answers, verified sources, or a finished document.2794 npmMIT
- FlicenseNot gradedqualityBmaintenanceEnables AI agents and MCP clients to run deterministic deep research consensus arbitration by parsing queries, evaluating source authority and contradictions, and returning structured JSON dossiers and telemetry.8-
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- AlicenseBqualityAmaintenanceEnables provenance-first scholarly retrieval, paper ingestion, and reproducible research workflows by searching academic and developer sources, extracting source-located facts and claims, and preserving evidence and provider uncertainty for MCP clients.371Apache 2.0
- AlicenseBqualityAmaintenanceEnables source-grounded prior-art and novelty research by searching patents, publications, and the web in one run, verifying evidence levels, and returning a structured report.7MIT
- AlicenseNot gradedqualityDmaintenanceEnables automated web research and intelligence gathering through recursive web crawling, multi-engine search integration, and persistent SQLite storage with support for keyword filtering and multiple export formats.MIT
TDQS
Scored across 6 tools
Each tool targets a distinct evidence layer: search_community covers lived experience, search_web covers published claims, search_demand covers transactions and workarounds, and read_discussion is an explicit follow-up for thread-level depth. The meta tools (corpus_stats, providers_status) are clearly separated from search operations, so there is no real ambiguity.
The search_* tools are consistently named with a clear verb_noun pattern, and read_discussion follows suit. corpus_stats and providers_status are descriptive and readable but don't follow the verb-first pattern, creating a minor stylistic inconsistency rather than confusion.
Six tools is a well-scoped size for a research assistant. Each tool earns its place: three distinct search layers, one deep-dive reader, and two diagnostic/infrastructure tools that support the search workflow without bloating the surface.
The set covers the core research loop well: finding lived-experience evidence, published claims, demand signals, reading full Reddit threads, checking corpus depth, and diagnosing provider issues. The main gaps are lack of direct arbitrary URL/content fetching and no obvious way to query the accumulated corpus itself beyond stats.