Paparats MCP: Self-Hosted Semantic Code Search MCP server

Paparats MCP

 ← try the full stack in your browser, no install (details)

Paparats-kvetka — a magical flower from Slavic folklore that blooms on Kupala Night and grants whoever finds it the power to see hidden things. Likewise, paparats-mcp helps your agent see the right code across a sea of repositories.

🌿 Works with Claude Code · Cursor · Windsurf · Copilot · Codex · Antigravity · any MCP-compatible agent

Give your AI coding assistant deep, real understanding of your entire workspace. Paparats indexes every repo you care about — semantically, with AST-aware chunking and a cross-chunk symbol graph — and exposes it through the Model Context Protocol. Search by meaning, follow who-uses-what through real symbol edges, see who last touched a chunk and which ticket it came from — all without your code ever leaving your machine.

📊 The built-in /ui operator console — ROI, query quality, cross-project usage, per-user activity, indexer health. Screenshot uses synthetic data (?demo=1) — no real queries, users, or project names.

file once and feeds both chunking and the cross-chunk symbol graph (calls / calledby / references / referencedby) — 11 languages including TypeScript, Python, Go, Rust, Java, Ruby, C, C++, C#.

• ⚡ One install, one config. paparats install → paparats add ~/code/repo → done.
• 🌳 AST-aware chunking and symbol extraction. Tree-sitter parses every supported

per group holds components, decisions (ADRs) and lessons learned — your agent writes them as it works and reads them before answering. Bootstrap on day one with the initarchmemory MCP prompt (the /init of architectural memory). Server-side similarity gate prevents duplicates, supersedes links replace stale decisions, a min_score threshold gates low-confidence reads, every card carries an "updated N ago" stamp, and Prometheus metrics tell you whether your memory is actually being used.

• 🧠 Architectural memory that the agent maintains itself. A second vector store

to prove it (per-query, per-user, per-anchor-project).

• 💸 Saves tokens. Returns only the chunks that matter, with token-savings telemetry

(Tempo, Jaeger, Honeycomb, Datadog, Grafana Cloud, Elastic APM), local SQLite analytics, and a built-in /ui operator console that visualises ROI, query quality, cross-project usage and indexer health in one screen.

• 🔭 Production-ready observability. Prometheus /metrics, OpenTelemetry traces

llama-swap) on your machine. No cloud, no API keys, no telemetry leaving the box. Bring your own Qdrant Cloud / embed server URL if you want.

• 🏠 100% local by default. Qdrant + a local embed server (llama.cpp llama-server +

Table of Contents

Shortened. The full README is on GitHub.