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u9401066

pubmed-search-mcp

by u9401066
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

build_citation_tree

Build a citation network from a PubMed article to map forward and backward research links, with output in multiple graph formats for analysis.

Instructions

Build a citation tree (network) from a single article.

🌳 Creates a visual citation network showing research lineage:

  • Forward (citing): Who cites this paper? (newer research)

  • Backward (references): What does this paper cite? (foundational work)

⚠️ IMPORTANT: Only accepts ONE PMID at a time to control API load. For multiple papers, call this tool separately for each.

📊 Output Formats (output_format parameter):

  • "cytoscape": Cytoscape.js format (default, academic standard)

  • "g6": AntV G6 format (modern, high-performance)

  • "d3": D3.js force graph format (flexible, Observable)

  • "vis": vis-network format (simple, quick prototypes)

  • "graphml": GraphML XML (desktop tools: Gephi, yEd, VOSviewer)

  • "mermaid": Mermaid diagram (VS Code preview, Markdown) ⭐NEW

Args: pmid: Single PubMed ID (e.g., "12345678"). Only ONE PMID accepted - do NOT pass multiple. depth: How many levels to traverse (1-3, default 2). - depth=1: Direct citations/references only - depth=2: Also get citations of citations (recommended) - depth=3: Maximum depth (can be slow, ~100+ API calls) direction: Which direction to build the tree: - "forward": Only citing articles (who cites this) - "backward": Only references (what this cites) - "both": Both directions (default, recommended) limit_per_level: Max articles to fetch per node per level (default 5) include_details: Include full article details (default True) output_format: Graph format for visualization (default "cytoscape") - "cytoscape": Cytoscape.js (academic standard, bioinformatics) - "g6": AntV G6 (modern, TypeScript, great for large graphs) - "d3": D3.js force layout (most flexible, Observable notebooks) - "vis": vis-network (simple and easy) - "graphml": GraphML XML (Gephi, VOSviewer, yEd, Pajek) - "mermaid": Mermaid diagram (preview in VS Code Markdown)

Returns: JSON string with graph data in the requested format. Includes metadata and statistics regardless of format.

Example usage: # Build 2-level tree for a paper (default Cytoscape.js format) build_citation_tree(pmid="33475315", depth=2, direction="both")

# Use AntV G6 format for modern web visualization
build_citation_tree(pmid="33475315", depth=2, output_format="g6")

# Export GraphML for Gephi analysis
build_citation_tree(pmid="33475315", depth=2, output_format="graphml")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pmidYes
depthNo
directionNoboth
limit_per_levelNo
include_detailsNo
output_formatNocytoscape

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses depth-related API call volume (depth=3 ~100+ calls), explains output formats and directions, and notes the return format. It does not mention rate limits or authentication, but overall transparency is good.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is quite detailed and uses effective formatting (emojis, bullets, examples) to enhance readability. While every sentence adds value, it could be slightly more concise without losing clarity, but the structure is good.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's 6 parameters, no annotations, and an assumed output schema, the description covers nearly all aspects: parameters, output format details, return value (JSON with metadata and statistics), and examples. It is comprehensive and prepares the agent well for invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must explain all parameters. It does so thoroughly: pmid (single, example), depth (1-3 with level descriptions), direction (three options), limit_per_level (default 5), include_details (default True), and output_format (eight options with use cases). This exceeds the typical parameter documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool builds a citation tree/network from a single article, distinguishing it from siblings like find_citing_articles or get_article_references. It uses specific verbs and resources, making the purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear usage guidance, including the important constraint that only one PMID is accepted at a time and suggesting separate calls for multiple papers. It does not explicitly list when not to use the tool or fully contrast with siblings, but the context is sufficient for most cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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