Show HN: Nia – MCP server that gives more context to coding agents

Hi HN, I’m Arlan, and I built Nia (https://www.trynia.ai), an open MCP that integrates with coding agents like Cursor, Continue, and Cline so they can retrieve external knowledge better than current approaches.

Coding agents generate code well but lose accuracy when the answer lives outside the repo in front of them. Developers end up pasting GitHub links, docs, and blog posts by hand and hoping the agent scrolls far enough. Long context windows help, but recent “context rot” measurements show quality still drops as prompts grow. For example, in LongMemEval, all models scored much higher on focused (short, relevant) prompts (~300 tokens) than on full (irrelevant, 113k tokens) prompts, with performance gaps persisting even in the latest models (https://research.trychroma.com/context-rot).

Nia is a MCP that gives more context to any coding agent or IDE. It Indexes multiple repos and docs sites and makes this available via MCP to your coding agent so it has much more context to work with, giving you more specific and accurate answers.

Nia uses a hybrid code search architecture that combines graph-based structural reasoning with vector-based understanding. When a repo or documentation is ingested, Tree-sitter parses it into ASTs across 50+ languages and natural languages, and the code is chunked by function/class boundaries into stable, content-addressable units. These chunks are stored both in a graph db to model relationships like function calls and class inheritance, and in a vector store. At query time, a lightweight agent with give_weight tool dynamically assigns weights between graph and vector search based on intent (e.g., "who calls X" vs "how does auth work"), and both paths are searched in parallel. Results are fused, enriched with full code context, and passed through multi-stage rerankers: semantic reranker, cross-encoders, LLM-based validators.

Early Signal: In internal evals we improved Cursor’s performance by 27 % once Nia had indexed external docs models couldn’t get from their training data or searching the web.

Quickstart: " rel="nofollow">