by matt-carey
MCP's context bloat problem has a clean solution: give the agent two tools—search and execute—running in a V8 sandbox against the full OpenAPI spec, cutting 1.17M tokens to ~1K.
by anthropic
Anthropic's 30+ page official guide covers the full skill lifecycle—from progressive disclosure architecture (frontmatter → SKILL.md → references) through five reusable workflow patterns, three-level testing, and organizational distribution—positioning skills as the knowledge layer that turns MCP's raw tool access into guided, reliable workflows.
by moshe-simantov
Documentation indexing for AI agents belongs on the local machine: pre-built SQLite databases eliminate rate limits, cut query latency to sub-10ms, and keep proprietary code queries completely private.
by playwright
Playwright CLI saves 4x tokens over MCP by writing browser data to files instead of piping it into the LLM context, making it the better choice for coding agents that can read from disk.
by anton-pidkuiko
An MCP server that gives Claude the ability to generate and render interactive Excalidraw diagrams directly in conversation, turning text prompts into hand-drawn visuals.
by alex-shershebnev
Building AI coding agents requires only basic tooling—a ReAct loop, tool definitions, and MCP integration—and the developer's role shifts from writing code to managing autonomous virtual developers.
by adam-gospodarczyk
MCP's flaws matter less than the fundamental LLM limitations it exposes: context bloat from tool schemas, degraded instruction following in long conversations, and inference costs that balloon with agentic workflows.
by neal-ford
MCP enables enterprise architects to express architectural intent without implementation details, solving the brittleness problem that has plagued fitness function governance at scale.
by zeframlou
A Claude Code plugin that phones you when the AI finishes a task, gets stuck, or needs a decision—enabling true asynchronous collaboration without constant monitoring.