uvx research-guard-mcp

{
  "mcpServers": {
    "research-guard": {
      "command": "uvx",
      "args": ["research-guard-mcp"],
      "env": {
        "S2_API_KEY": "your-semanticscholar-api-key",
        "OPENAI_API_KEY": "your-openai-api-key"
      }
    }
  }
}

claude mcp add research-guard -- uvx research-guard-mcp

{
  "mcpServers": {
    "research-guard": {
      "command": "uvx",
      "args": ["research-guard-mcp"],
      "env": {
        "S2_API_KEY": "your-semanticscholar-api-key",
        "OPENAI_API_KEY": "your-openai-api-key"
      }
    }
  }
}

Your research idea
       |
   [Decompose]       →  (Problem, Method, Innovation) triple
       |
   [Expand]          →  5-dimension concept expansion
       |                  (synonyms, ancestors, alternatives, critiques, cross-domain)
       |
   [Retrieve]        →  Semantic Scholar + OpenAlex (parallel, deduplicated)
       |
   [Quick Rank]      →  Contribution-point scoring (text + semantic similarity)
       |
   [Deep Compare]    →  LLM pairwise comparison with evidence grounding
       |
   [Assess]          →  RED / YELLOW / GREEN  +  overlaps  +  blind spots

You: I'm thinking of using vision transformers with shifted windows
     for 3D medical image segmentation. Is this novel?

Claude: [calls check_novelty on Research Guard]

Research Guard result:
  Rating: YELLOW (partially overlapping)
  - Swin UNETR (2022) already uses Swin Transformer for brain tumor segmentation
  - TransUNet (2021) combines transformers with U-Net for medical images
  - Differentiation space: your 3D shifted window approach differs from their
    2D slice-based methods
  - Blind spot: no counter-evidence found for this combination

Claude: Your idea has significant overlap with existing work, but there's
        a clear differentiation path — the 3D shifted window mechanism is
        novel in this domain. I'd recommend proceeding with explicit
        positioning against Swin UNETR.

research-guard/
├── packages/
│   ├── guard_core/          # Core engine (no MCP dependency)
│   │   ├── models.py        # Pydantic v2 type system
│   │   ├── decomposer.py    # LLM-based idea decomposition
│   │   ├── expander.py      # 5-dim concept expansion
│   │   ├── retriever.py     # Multi-source parallel retrieval
│   │   ├── novelty_checker.py # Main pipeline orchestrator
│   │   ├── similarity.py    # Text + semantic scoring
│   │   ├── blind_spot.py    # Counter-evidence detection
│   │   ├── contribution_comparator.py  # Contribution-level comparison
│   │   ├── cache.py         # SQLite async cache
│   │   ├── cost_tracker.py  # LLM cost monitoring
│   │   └── sources/         # API clients (S2, OpenAlex, arXiv)
│   ├── guard_mcp/           # FastMCP server (thin adapter)
│   └── guard_web/           # [Phase 2b] Web UI
├── plugins/                 # Extensible data source ABCs
├── datasets/                # Domain vocabularies + validators
├── tests/
│   ├── unit/                # Pure logic tests
│   └── integration/         # Real API tests (skippable)
└── docs/

git clone https://github.com/Fengrru/research-guard.git
cd research-guard
uv sync

# Run all tests
uv run pytest tests/unit -v

# Lint
uv run ruff check packages/ plugins/ datasets/ tests/

# Type check
uv run mypy packages/ --ignore-missing-imports