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.}

• ⚡ One install, one config. paparats install → paparats add ~/code/repo → done.
• 🌳 AST-aware chunking and symbol extraction. Tree-sitter parses every supported file once and feeds both chunking and the cross-chunk symbol graph (calls / called_by / references / referenced_by) — 11 languages including TypeScript, Python, Go, Rust, Java, Ruby, C, C++, C#.
• 🧠 Architectural memory that the agent maintains itself. A second vector store 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 init_arch_memory 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.
• 💸 Saves tokens. Returns only the chunks that matter, with token-savings telemetry to prove it (per-query, per-user, per-anchor-project).
• 🔭 Production-ready observability. Prometheus /metrics, OpenTelemetry traces (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.
• 🏠 100% local by default. Qdrant + a local embed server (llama.cpp llama-server + 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.

Table of Contents

• Why Paparats?
• Quick Start
• How the install works
• Install variants
• Migrating from a v1 install
• Support agent setup
• How It Works
• Key Features
• Use Cases
• Architectural memory
• Configuration
• MCP Tools Reference
• Connecting MCP
• CLI Commands
• Monitoring
• Analytics & Observability
• Architecture
• Embedding Model Setup
• Comparison with Alternatives
• Token Savings Metrics
• Contributing
• Links

Why Paparats?

AI coding assistants are smart, but they can only see files you open. They don't know your codebase structure, where the authentication logic lives, or how services connect. Paparats fixes that.

What you get

• Semantic code search — ask "where is the rate limiting logic?" and get exact code ranked by meaning, not grep matches
• Real-time sync — edit a file, and 2 seconds later it's re-indexed. No manual re-runs
• Cross-chunk symbol graph — find_usages walks AST-derived edges (calls, called_by, references, referenced_by) so the agent can trace dependencies without re-grepping
• Token savings — return only relevant chunks instead of full files to reduce context size
• Multi-project workspaces — search across backend, frontend, infra repos in one query
• 100% local & private — Qdrant vector database + local llama-server embeddings. Nothing leaves your laptop
• AST-aware chunking — code split by AST nodes (functions/classes) via tree-sitter, not arbitrary character counts (TypeScript, JavaScript, TSX, Python, Go, Rust, Java, Ruby, C, C++, C#; regex fallback for Terraform)
• Rich metadata — each chunk knows its symbol name (from tree-sitter AST), service, domain context, and tags from directory structure
• Git history per chunk — see who last modified a chunk, when, and which tickets (Jira, GitHub) are linked to it
• Architectural memory — a living knowledge base of components, decisions (ADRs) and lessons learned, written by the agent as it learns, deduplicated server-side by vector similarity, and consulted on every support query so the agent stays consistent across sessions

Who benefits

Quick Start

Try it in the browser (no install)

Spin up a full Qdrant + embed server + paparats stack in a Codespace. A small slice of the repo (packages/shared/src) is auto-indexed on first start so you can run

paparats search -g demo 'gitignore filter'

within a few minutes. Codespace forwards port 9876 for MCP — point Cursor/Claude Code at it via the URL VS Code shows in the Ports panel.

Note: Codespaces is for demo only. With CPU embedding the full repo would take 15+ minutes and can hit batch timeouts on large files. For real workloads run locally — or set OPENAI_API_KEY (or VOYAGE_API_KEY) as a Codespaces user secret and indexing drops to a couple of seconds; see the Embedding providers section below.

Run locally

You need Docker and Docker Compose v2. On macOS, also install the embed server natively — running it inside Docker on macOS is significantly slower because the Docker VM cannot use Apple Silicon GPU (Metal) acceleration.

# 1. Install the CLI.
npm install -g @paparats/cli

# 2. macOS only — install the native embed server (Linux uses the Docker embed
#    image by default). Metal-accelerated.
brew install llama.cpp mostlygeek/llama-swap/llama-swap

# 3. One-time bootstrap. Generates ~/.paparats/{docker-compose.yml,projects.yml},
#    starts the stack, downloads the embedding model, wires Cursor/Claude Code MCP.
paparats install

# 4. Add the projects you want indexed. Local paths bind-mount read-only into the
#    indexer; git URLs and owner/repo shorthand get cloned.
paparats add ~/code/my-project
paparats add git@github.com:acme/billing.git
paparats add acme/widgets

# 5. Watch it work.
paparats list

That's it. Your IDE is already wired (~/.cursor/mcp.json, ~/.claude/mcp.json) to http://localhost:9876/mcp. Open Cursor or Claude Code and ask:

"Search this workspace for the auth middleware and show me everything that calls it."

Existing v1 user?

Just run paparats install again. The installer detects the legacy per-project compose, asks once before swapping it for the new global setup, and preserves your indexed data (Qdrant collections, SQLite metadata, embedding cache). Your in-repo .paparats.yml files keep working as per-project overrides.

How the install works

paparats install is the only setup command. It creates a single global home at ~/.paparats/, brings up a Docker stack, and wires your MCP clients. Re-run it any time to reconfigure — it diffs the existing compose and asks before overwriting hand edits.