networkx

NetworkX MCP Server provides academic-focused graph analysis capabilities integrated into your AI conversations. It exposes a suite of functions for building and analyzing citation networks, calculating author impact metrics, and performing core graph operations via a conversational interface. From resolving DOIs and constructing citation networks to computing PageRank and identifying collaboration patterns, you can access a rich set of tools to support literature reviews, meta-analyses, and research trend detection directly within your chats. The server leverages Python and NetworkX to enable reproducible graph analyses that are shareable with teammates or students.

To use the server, install the package and run the MCP server through Python, then configure your Claude Desktop or compatible MCP client to point at the server. The available operations cover academic research, core graph operations, analysis, and visualization/I-O, including: resolve_doi, build_citation_network, analyze_author_impact, find_collaboration_patterns, detect_research_trends, recommend_papers, export_bibtex, and core graph commands like create_graph, add_nodes, add_edges, get_info, and shortest_path. You can also visualize graphs and import/export formats to fit your workflow.

In practice, you’ll interact with the server by sending requests that specify an operation and the required parameters (e.g., seed DOIs, graph specifications, or analysis settings). The MCP-enabled tool will return structured results such as metadata, network statistics, centrality scores, and BibTeX exports, enabling you to iterate on literature reviews and analyses within your AI-assisted sessions.

Prerequisites:

• Python 3.11 or newer
• Pip (comes with Python)
• Internet access to install the MCP package

1. Create and activate a Python virtual environment (optional but recommended):

python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate

2. Install the NetworkX MCP Server package from PyPI:

pip install networkx-mcp-server

3. Run the MCP server (as shown in the repo README example):

python -m networkx_mcp

4. Configure your MCP client (e.g., Claude Desktop) to connect to the server. Example configuration (in claude_desktop_config.json):

{
  "mcpServers": {
    "networkx": {
      "command": "python",
      "args": ["-m", "networkx_mcp"]
    }
  }
}

5. Verify installation by invoking a basic operation such as creating a graph and querying its info via the MCP client.

Tips and common considerations:

• Ensure you are using Python 3.11+ to match the package requirements.
• If you encounter port or networking issues, verify that your MCP client can reach the running Python process and that there are no firewall blocks.
• The package exposes a range of academic tools; for best results provide accurate seeds (DOIs or paper identifiers) when building citation networks.
• BibTeX export is available for integrating results into LaTeX workflows.
• If you need to run multiple instances, consider isolated virtual environments or containerization to avoid dependency conflicts.
• Review API/CLI docs in the repository for any advanced configuration options such as CrossRef API usage or visualization parameters.