context-space and context-engineering-mcp

context-space
49
Emerging
context-engineering-mcp
27
Experimental
Maintenance 6/25
Adoption 10/25
Maturity 15/25
Community 18/25
Maintenance 2/25
Adoption 5/25
Maturity 7/25
Community 13/25
Stars: 805
Forks: 81
Downloads:
Commits (30d): 0
Language: Go
License: AGPL-3.0
Stars: 10
Forks: 2
Downloads:
Commits (30d): 0
Language:
License:
No Package No Dependents
No License Stale 6m No Package No Dependents

About context-space

context-space/context-space

Ultimate Context Engineering Infrastructure, starting from MCPs and Integrations

This infrastructure helps AI agents or automation workflows access real-world services and data securely and efficiently. It takes scattered APIs and data sources from services like GitHub, Slack, and Notion, and provides a unified, secure connection for AI agents to interact with them. This is ideal for developers and AI engineers building and deploying AI agents that need to perform actions or retrieve information across various business applications.

AI Agent Development Automation Engineering API Integration Workflow Orchestration Enterprise AI

About context-engineering-mcp

bralca/context-engineering-mcp

Context Engineering is a MCP server that gives AI agents perfect understanding of your codebase. Eliminates context loss, reduces token usage, and generates comprehensive feature plans in minutes. Compatible with Cursor, Claude Code, and VS Code.

This project helps software developers and development teams leverage AI assistants like Cursor or Claude Code to build features faster and more consistently. It takes your existing codebase, understands its structure, tech stack, and coding patterns, then uses this deep context to generate comprehensive feature plans, technical documentation, and task lists. The result is AI-generated code and plans that perfectly align with your project's established conventions.

software-development feature-planning codebase-management technical-documentation AI-assisted-development

Scores updated daily from GitHub, PyPI, and npm data. How scores work