
Cursor
Desktop · Freemium · Proprietary
The first agentic IDE. The Cursor editor truly merges how developers and AI work together, delivering a magical coding experience.

graphistry/graphistry-mcp
This project integrates Graphistry's powerful GPU-accelerated graph visualization platform with the Model Control Protocol (MCP), enabling advanced graph analytics capabilities for AI assistants and LLMs. It allows LLMs to visualize and analyze complex network data through a standardized, LLM-friendly interface.
npx -y @silkspace/graphistry-mcpGRAPHISTRY_USERNAMEGRAPHISTRY_PASSWORDGPU-accelerated graph visualization and analytics for Large Language Models using Graphistry and MCP.
visualize_graph
Visualize a graph or hypergraph using Graphistry's GPU-accelerated renderer.
get_graph_info
Get metadata (node/edge counts, title, description) for a stored graph.
apply_layout
Apply a standard layout (force_directed, radial, circle, grid) to a graph.
detect_patterns
Run network analysis (centrality, community detection, path finding, anomaly detection).
encode_point_color
Set node color encoding by column (categorical or continuous).
encode_point_size
Set node size encoding by column (categorical or continuous).
encode_point_icon
Set node icon encoding by column (categorical, with icon mapping or binning).
encode_point_badge
Set node badge encoding by column (categorical, with icon mapping or binning).
apply_ring_categorical_layout
Arrange nodes in rings by a categorical column (e.g., group/type).
apply_group_in_a_box_layout
Arrange nodes in group-in-a-box layout (requires igraph).
apply_modularity_weighted_layout
Arrange nodes by modularity-weighted layout (requires igraph).
apply_ring_continuous_layout
Arrange nodes in rings by a continuous column (e.g., score).
apply_time_ring_layout
Arrange nodes in rings by a datetime column (e.g., created_at).
apply_tree_layout
Arrange nodes in a tree (layered hierarchical) layout.
set_graph_settings
Set advanced visualization settings (point size, edge influence, etc.).
Details on this page are taken from the project's README. Open README
Clients mentioned in this server's README:

mendableai/firecrawl-mcp-server
A Model Context Protocol (MCP) server that brings Firecrawl to MCP-compatible AI agents — search, scrape, and interact with the live web for clean, agent-ready context.

exa-labs/exa-mcp-server
Connect AI agents to Exa for web search, content fetching, and multi-step research.

blazickjp/arxiv-mcp-server
A local MCP server for agent literature work. The differentiator is original-LaTeX section reads, BibTeX from arXiv metadata, and topic watches. Papers stay on disk.

lakehq/sail
Sail is a drop-in Apache Spark replacement written in Rust, unifying batch processing, stream processing, and compute-intensive AI workloads on a distributed, multimodal compute engine.
janwilmake/openapi-mcp-server
A Model Context Protocol (MCP) server for Claude/Cursor that enables searching and exploring OpenAPI specifications through oapis.org.

haris-musa/excel-mcp-server
A Model Context Protocol server that lets AI assistants create, read and edit Excel workbooks. It needs no Microsoft Excel installation.