mcp
20 notes tagged mcp in the AI agents topic. How agents are built, tuned and run: loops, harnesses, MCP, skills and coding agents, with what broke and what we measured.
- Five layers to tune before you fork your agent
What makes AI agentic, how to split a deterministic core from an agentic edge, and the five layers you can tune without rebuilding the harness. Real numbers from circle…
- Context7 breakdown
Technical analysis of Context7, an intelligent documentation indexing and retrieval system that transforms raw library docs into AI-optimized, ranked snippets for…
- Zen MCP breakdown
Technical analysis of the Zen MCP (Model Context Protocol) Server architecture, implementation, and design patterns.
- Understanding Tidewave.ai
Discover how Tidewave connects AI assistants to your web framework's runtime through MCP, enabling real-time interaction with your application. Learn about its features,…
- Composing the future, part 1: agent basics, LangGraph and Autogen
What makes a system agentic, why multi-agent beats single-agent, how orchestration works, and a close look at LangGraph and Microsoft Autogen.
- Composing the future, part 2: OpenAI's agent tools, MCP and fast-agent
How OpenAI's Swarm and newer agent tools work, why Model Context Protocol matters for tool access, and how fast-agent builds MCP-native agents.
- Composing the future, part 3: Mastra and CrewAI
Two frameworks with opposite instincts: Mastra for TypeScript teams building web-native agents, and CrewAI for role-playing crews of Python agents.
- Composing the future, part 4: LlamaIndex and the framework face-off
LlamaIndex's agent features for document-heavy work, then a side-by-side comparison of the frameworks on differentiators, strengths and ease of development.
- Composing the future, part 5: agent patterns, open problems and how to choose
The common agent composition patterns, the problems that remain unsolved, where the field is heading, and guidance for picking a framework.
- MCP playbook data flow
How data flows through the MCP playbook server, from MCP client requests to tool execution and interactions with the file system and GitHub. This overview helps you…
- MCP playbook setup
A step-by-step tutorial on configuring and running the mcp-playbook server, including GitHub token creation and using npx. This guide helps you get started with…
- Build a brain that never forgets
Explore how we solved the catastrophic forgetting problem in LLMs by building an external knowledge system that mimics learning through context manipulation, without…
- Data flow in Brainery
How data flows through Brainery's second brain system, from Discord, Memo Blog, and GitHub sources to TimescaleDB via MCP, with an LLM-powered interface for natural…
- MCP authorization over SSE
A Request for Comments (RFC) detailing a comprehensive specification for implementing OAuth 2.1 authorization within the Model Context Protocol (MCP) when using…
- Access the brainery
This page outlines how developers can connect to and utilize Brainery's extensive knowledge base and AI-driven capabilities. By integrating a straightforward MCP…
- How to use Claude to build the system prompt
Building the use case: how to use Claude to build the system prompt
- Authorization for remote MCP servers over SSE
This guide explores implementing robust authorization for Model Context Protocol (MCP) over Server-Sent Events (SSE) transport, providing a standardized framework for…
- Tool-level security for remote MCP servers, part 1: designing role-based access control
Why remote MCP servers need tool-level access control, the security architecture, and the data model and tool registry behind role-based access.
- Tool-level security for remote MCP servers, part 2: enforcement, identity and practice
How an MCP server enforces permissions and filters data, connects to existing identity systems, and rolls out access control in practice.