#MCP
7 articles exploring MCP. Expert insights and analysis from our editorial team.
Articles
Inside Rowboat's Knowledge Graph: Why an Obsidian-Compatible Vault Sidesteps Vector DBs for Personal AI Memory
Rowboat v0.3.1 replaces the vector DB tier with a plain Markdown knowledge graph, cutting infra overhead for local-first agents but tying retrieval quality to link density.
MCP STDIO Executes Even When the Server Fails: One Design Decision, 14 CVEs, 30+ RCEs
[OX Security's April 2026 advisory](/articles/vercels-april-2026-database-leak-pivoted-from-lumma-stealer-at-context-ai-via/) traces 14 CVEs and 30+ RCEs across LiteLLM, Flowise, and Cursor to one MCP STDIO behavior: the command field executes before handshake.
A2A v1.0 Left Agent Discovery Blank: Why AAIF's 170-Member Standard Still Forces Every Enterprise to Build Its Own Governance Layer
A2A v1.0 defines Agent Cards but deliberately leaves registry, discovery, and governance infrastructure unspecified, forcing every enterprise to build its own.
Cloudflare Browser Run's CDP and MCP Support: Serverless Browser Automation for AI Agents
Cloudflare renamed Browser Rendering to Browser Run in April 2026 and added CDP and MCP support, letting AI agents use managed headless Chrome with a single config change.
The MCP Registry: GitHub's Play to Become the App Store for AI Tools
GitHub's MCP Registry centralizes discovery of Model Context Protocol servers, positioning GitHub as the primary distribution layer for AI agent tooling and addressing the fragmentation that emerged as MCP's ecosystem exploded past 5,000 servers in under a year.
MCP Is Everywhere: The Protocol That Connected AI to Everything
How the Model Context Protocol became the universal standard connecting AI assistants to data sources, tools, and enterprise systems—transforming isolated models into truly connected agents.
Building MCP Servers: Extending Claude's Capabilities with the Model Context Protocol
A comprehensive guide to building Model Context Protocol (MCP) servers that extend Claude's capabilities. Learn the architecture, implementation patterns, and best practices for creating powerful AI integrations.