context-engineering MCP Server
bonigarcia/context-engineering
[](https://agentrank-ai.com/tool/bonigarcia--context-engineering/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge) Context Engineering: Build Consistent, Accurate, Predictable AI Systems
claude mcp add agentrank -- npx -y agentrank-mcp-server Overview
bonigarcia/context-engineering is a Python tool licensed under Apache-2.0. Context Engineering: Build Consistent, Accurate, Predictable AI Systems Topics: agentic-ai, context-engineering, mcp, memory-management, multi-agent-systems, prompting, rag, llm, generative-ai, mcp-server, agent-skills, spec-driven-development.
Ranked #3355 out of 24846 indexed tools.
Ecosystem
Score Breakdown
165 stars → growing community
Last commit 29d ago → recently updated
1/2 issues closed → decent issue management
5 contributors → small team
No dependents → no downstream usage
Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →
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From the README
# Context Engineering **Context engineering** can be defined as the practice of designing systems that provide a Large Language Model (LLM) and AI agents with all the necessary information to complete a task effectively. It goes beyond prompt engineering since it focuses on building a comprehensive and structured context from various sources like instructions, external knowledge, memory, tools, and state. The central idea is that the success of a complex LLM-based system depends more on the quality and completeness of the context provided than on the specific wording of the prompt itself. Tobi Lütke, the CEO of Shopify, coined the term _context engineering_ in a [tweet](https://x.com/tobi/status/1935533422589399127) on June 19, 2025. He defined context engineering as _the art of providing all the context for the task to be plausibly solvable by the LLM_. This novel concept captures the essence of the current evolution of LLM-based systems, inspiring others (like me) to understand anRead full README on GitHub →
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