Enables MCP clients like Claude Code and Codex to delegate coding tasks to Cursor's CLI agent, which implements changes in the workspace and returns clean, structured results for review.
Provides structured external memory for AI assistants, enabling persistent context, branch notes, tacit knowledge, and checklists to overcome AI memory loss and context confusion.
A local MCP server that improves token efficiency for Cursor's agent by providing cheaper tools like search_code, outline_file, read_snippet, project_map, and handoff, along with rules that steer the agent to use them and reduce excessive context usage.
An MCP server that audits a project for documentation, rules, skills, and knowledge artifacts, compares against a reference concept catalog, and generates a prescription plan with frontmatter todos to guide creation of missing or incomplete elements.
MCP server that brings Trustabl static reliability scanning into Cursor, letting agents inventory agents/tools, flag risky patterns, score production readiness, and write SARIF reports.
Facilitates integration with the Cursor code editor by enabling real-time code indexing, analysis, and bi-directional communication with Claude, supporting concurrent sessions and automatic reconnection.
MCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models through the MCP, enabling AI models to:
Submit and validate constraint models
Set model parameters
Solve constraint satisfaction and optimization problems
Retrieve and analyze solution
Monitors your Cursor usage and provides a conserve flag to the agent, helping you manage request quota by batching questions, preferring defaults, and cutting unnecessary confirmations.
Allows AI assistants to access and analyze your Cursor conversation history, enabling personalized coding assistance based on your actual development patterns.
Provides tools for Cursor to interact with IBM Bob Shell, enabling Bob-powered coding assistance, session management, and task delegation directly from Cursor.
Enables any MCP-compatible AI tool to read and leverage Cursor's context files (rules, agents, and skills) with their activation logic, allowing teams to maintain a single source of context across different AI coding assistants.