
What's new in Nuanced: resetting trials and simplifying commands
With LSP now live in Nuanced MCP, we're resetting trials to day zero and rolling out simplified commands, a new NPM package, and integrated LSP tools.
Most agents hallucinate. Nuanced doesn't let them.
Our MCP server powers coding agents like Cursor, Claude Code, and Codex with call graphs and symbol data so edits land right the first time.
We're ex-GitHub engineers who built the code intelligence behind GitHub search at scale. We know compiler-grade context can make agents dramatically better.
AI coding tools often rely on manual, trial-and-error context engineering: stuffing prompts with tokens or sending agents to roam the repo. It's slow, brittle, and misses what actually matters: how your code connects and what happens at runtime.
Nuanced gives agents compiler-grade knowledge: call graphs, symbol and type facts, dataflow and control flow. The result is faster responses, edits you can trust, fewer wasted tokens, and context that stays fresh automatically as your codebase grows.
With LSP now live in Nuanced MCP, we're resetting trials to day zero and rolling out simplified commands, a new NPM package, and integrated LSP tools.
From TypeScript call graphs to symbol-level precision across eight languages—built for coding agents.
Nuanced MCP brings structured static analysis to copilots, so they focus on the right context, not just more tokens.
Nuanced MCP brings structured static analysis to copilots, so they focus on the right context, not just more tokens.
The speed of natural language with the precision of compilers.
We're bringing sound, LLM-ready call graph context to TypeScript.
Introducing Nuanced, the open-source library that gives AI coding assistants and developers the deep code understanding they've been missing.
How giving AI coding assistants access to system-wide context—from memory profiles to error reports—transforms their debugging capabilities.
Nuanced helps AI find relevant context in code by building intelligent knowledge graphs.
As AI accelerates code generation, developers need better tools to understand and maintain the increasingly complex systems they're building.