BLOG / MCP
MCP & AI Agents
Model Context Protocol, AI agent integration, and codebase context for LLMs
When an MCP tool should return raw text, not answers
A practical mcp tools design rule: return raw evidence when the model needs to inspect files, symbols, or decisions. See the boundary in Repowise.
Why we ship a graph-aware MCP server instead of file readers
Graph aware mcp cuts grep loops: resolve callers, dependents, and review impact in one hop. See the token and latency math.
Task-Shaped MCP Tools Beat Entity CRUD for Code Agents
See mcp tools design on a real regression: task-shaped tools cut re-reading and context loss, with 49% fewer calls and 27× fewer tokens.
8 MCP Tools That Make AI Agents Actually Useful for Code
The current state of AI-assisted engineering feels like a paradox. We have Large Language Models (LLMs) with massive context windows—some spanning millions o...
Give Your AI Agent Codebase Context Without Stuffing the Prompt
The engineering community is currently caught in a 'context arms race.' As LLM providers announce increasingly massive context windows—moving from 32k to 200...
How to Set Up an MCP Server for Claude Code, Cursor, and Cline
The fundamental bottleneck in AI-assisted engineering isn't the model's reasoning capability; it’s the context window. Even with million-token windows, stuff...
What Is MCP (Model Context Protocol) and Why Your Codebase Needs a Server
We’ve all been there: staring at a 200,000-line repository, trying to explain the intricate dance of a distributed state machine to an LLM. You copy-paste th...