Citation Graphs¶

Scholar MCP provides four tools for exploring citation networks. This guide covers common patterns and strategies.

Tools overview¶

Tool	Use case
`get_citations`	"What papers cite this paper?"
`get_references`	"What papers does this paper cite?"
`get_citation_graph`	"Show me the citation network around these papers"
`find_bridge_papers`	"How are these two papers connected?"

Basic exploration¶

Forward citations¶

Find papers that cite a known paper:

get_citations("DOI:10.48550/arXiv.2005.11401", limit=20)

Use filters to narrow results:

get_citations(
    "DOI:10.48550/arXiv.2005.11401",
    year_start=2023,
    min_citations=10,
    fields_of_study=["Computer Science"]
)

Backward references¶

Find papers cited by a given paper (its bibliography):

get_references("DOI:10.48550/arXiv.2005.11401", limit=50)

Graph traversal¶

get_citation_graph performs a BFS (breadth-first search) from seed papers, collecting nodes and edges.

Single-seed graph¶

Start from one paper, explore its citation neighborhood:

get_citation_graph(
    seed_ids=["CorpusId:218971783"],
    direction="both",
    depth=1,
    max_nodes=50
)

Multi-seed graph¶

Start from multiple papers to map a research area:

get_citation_graph(
    seed_ids=["CorpusId:218971783", "CorpusId:237491837"],
    direction="citations",
    depth=2,
    max_nodes=100
)

Direction strategies¶

Direction	Use case
`citations`	Find follow-up work and applications
`references`	Trace intellectual foundations
`both`	Map the full neighborhood around a paper

Controlling graph size¶

Start small

Begin with depth=1 and max_nodes=50. Citation graphs grow exponentially with depth -- a popular paper at depth=2, direction=both can easily hit 10,000+ nodes.

The max_nodes parameter is a hard cap. Once reached, the BFS stops and stats.truncated is set to true. Filters help focus the graph:

get_citation_graph(
    seed_ids=["CorpusId:218971783"],
    direction="both",
    depth=2,
    max_nodes=100,
    year_start=2020,
    min_citations=5
)

Reading the results¶

The response contains:

{
  "nodes": [{"id": "...", "title": "...", "year": 2024, "citationCount": 42}],
  "edges": [{"source": "id1", "target": "id2"}],
  "stats": {
    "total_nodes": 42,
    "total_edges": 67,
    "depth_reached": 2,
    "truncated": false
  }
}

nodes -- full paper metadata for each paper in the graph
edges -- directed edges from citing paper to cited paper
stats.truncated -- if true, the graph was cut short by max_nodes

Bridge papers¶

find_bridge_papers finds the shortest citation path between two papers. This is useful for:

Discovering how two research areas are connected
Finding seminal papers that bridge fields
Understanding intellectual lineage between works

find_bridge_papers(
    source_id="CorpusId:218971783",
    target_id="CorpusId:237491837",
    max_depth=4,
    direction="citations"
)

The response includes the full path with metadata for each paper:

{
  "found": true,
  "path": [
    {"paperId": "source", "title": "Attention Is All You Need", ...},
    {"paperId": "bridge", "title": "BERT: Pre-training of...", ...},
    {"paperId": "target", "title": "GPT-4 Technical Report", ...}
  ]
}

Tips¶

Direction matters: citations follows forward citations (A cites B); references follows backward references. Try both if one direction doesn't find a path.
Increase depth carefully: Each additional depth level can make many API calls. Start with max_depth=3 or 4.
Caching helps: Citation and reference lists are cached for 7 days. Repeated bridge searches in the same area are fast.

Caching behavior¶

Data	TTL	Effect
Paper metadata	30 days	Nodes reused across graph and bridge queries
Citation lists	7 days	`get_citations` results reused by graph BFS
Reference lists	7 days	`get_references` results reused by graph BFS
ID aliases	permanent	DOI-to-S2-ID mappings never expire

The cache significantly reduces API calls for repeated exploration in the same area. A get_citation_graph at depth=2 that returns 100 nodes might only make 10-20 API calls if the neighborhood was partially explored before.