research-scope-mcp
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- AlicenseNot gradedqualityBmaintenanceEnables autonomous multi-perspective deep research, evidence harvesting, and verified report synthesis with confidence scoring, directly from MCP-compatible clients.MIT
- AlicenseAqualityAmaintenanceEnables AI agents and MCP clients to run research as an explicit scientific process: forming hypotheses, designing and executing sealed experiments, capturing observations and artifacts, linking claims to supporting evidence, and reviewing provenance. Exposes coordinated MCP services so users can inspect, reproduce, and audit every step from question to conclusion rather than trusting an opaque final answer.46162 PyPI21Apache 2.0
- FlicenseNot gradedqualityBmaintenanceEnables decomposition of complex research queries into multi-hop sub-queries and synthesis DAGs, with scientific consensus analysis, citation credibility verification, and deterministic JSON outputs for MCP-compliant clients.8-
- AlicenseCqualityBmaintenanceGives any MCP client 27 tools for turning scattered feeds, forums, and media into a persistent, searchable evidence corpus, so an agent can search and import evidence, verify claims with independent-source counting and citation checks, and run multi-round research that produces cited, revisable reports. It also supports adaptive retrieval widening, tiered evidence where only author-written sections count as sources, and honest degradation when no LLM key is available.27MIT
- 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
- AlicenseNot gradedqualityCmaintenanceEnables local MCP clients to orchestrate a source-aware volunteer-briefing workflow with pause and takeover, incremental source checks, and exact-draft confirmation and download.MIT
TDQS
Scored across 4 tools
Each tool occupies a distinct stage of a linear research pipeline: plan_research (planning), fetch_and_score (acquisition), build_report (assembly), and check_report (verification). There is no overlap in purpose, so an agent can select unambiguously.
All names use snake_case with a verb-led pattern (plan_research, build_report, check_report), which is predictable and readable. fetch_and_score is the one deviation, using a compound verb_and_verb form rather than verb_noun, but it remains clear.
Four tools is well-scoped for a focused research workflow, with each tool earning its place as a pipeline stage. It is slightly lean, as there is no dedicated source-discovery step, but nothing is redundant.
The surface covers the full lifecycle: plan, fetch+score, build, and verify, including a citation registry and orphan-claim checking. The main gap is source discovery/search, leaving the agent to supply URLs itself, but core workflows have no dead ends.