Getting Started with Snipara

Get up and running with Snipara in under 5 minutes. This guide will walk you through creating your first project and connecting your MCP client.

Key Takeaways

  • 5-minute setup — Create project, get API key, add MCP config
  • 90% cost reduction — From 500K tokens to ~5K relevant tokens
  • Works with any LLM — Claude, GPT, Gemini, and more
  • No vendor lock-in — We optimize context, you use your own LLM

What is Snipara?

Snipara is a Context Optimization as a Service platform. We do not run an LLM — we optimize and deliver relevant context to your own LLM.

Fastest Way: NPX Setup Recommended

One command installs Snipara MCP + RLM-Runtime and configures everything automatically.

npx create-snipara

Prompts for API key, detects your AI client (Claude Code, Cursor, etc.), creates .mcp.json, and optionally configures execution environments (sandbox/docker/local).

60-Second Setup

Create project in Dashboard
Copy API key
Add MCP config
Start querying
{
  "mcpServers": {
    "snipara": {
      "type": "http",
      "url": "https://api.snipara.com/mcp/YOUR_PROJECT_SLUG",
      "headers": { "X-API-Key": "rlm_YOUR_API_KEY" }
    }
  }
}

Use your project slug (e.g., my-project) or ID. Then ask your LLM: "How does authentication work?"

Detailed Setup

Step 1: Create an Account

Sign up for a free Snipara account. You will get 100 free queries per month to try out the service.

Step 2: Create a Project

Once logged in, navigate to the Dashboard and click "New Project". Give your project a name and optional description.

https://snipara.com/dashboard/projects

Dashboard - Projects View

Step 3: Upload Your Documentation

You can upload documentation in several ways:

  • Manual upload - Upload markdown files directly through the web interface
  • API upload - Use our REST API to programmatically add documents
  • GitHub sync - Connect your repository for automatic syncing (Pro+ plans)

Step 4: Get Your Credentials

Go to your project's API Keys tab in the dashboard. Here you will find:

  • Project ID or Slug - Use either the database ID or the human-readable slug in your MCP URL
  • API Key - Click "Create API Key" to generate one
Project Slug (recommended - easier to remember)
my-project
Project ID (also works)
clxxxxxxxxxxxxxxxxxxxxxxxxxxx
API Key
rlm_xxxxxxxxxxxxxxxxxxxxxxxx

Tip

Use your project slug (e.g., my-project) instead of the ID for a cleaner, more memorable MCP URL.

Security Warning

Keep your API key secret. Never commit it to version control or share it publicly.

Step 5: Connect Your MCP Client

Add the MCP configuration shown in the 60-Second Setup above to your client. See our Integration Guide for detailed instructions for Claude Code, Cursor, Continue.dev, and other clients.

Advanced: Near-Infinite Context with RELP

For complex questions that span multiple documentation sections, Snipara offers RELP (Recursive Execution Loop Pipeline) — tools that automatically decompose queries and retrieve context efficiently.

Reduced Hallucination

RELP grounds responses in your actual documentation, providing source-attributed answers.

  • rlm_decompose — Break complex queries into sub-queries (Pro+)
  • rlm_multi_query — Execute multiple queries with shared budget (Pro+)
  • rlm_plan — Generate execution plans for complex tasks (Team+)

With RELP, handle documentation 100x larger than your context window.

Advanced: RLM Runtime for Complex Operations

For complex multi-step tasks that require code execution, recursive decomposition, or deep context retrieval, use rlm-runtime — a Python CLI and library that combines sandboxed code execution with Snipara's context optimization.

pip install rlm-runtime[snipara]
rlm run "Implement user authentication with email verification"

What RLM Runtime Does

  • Task Decomposition — Automatically breaks complex tasks into sub-tasks
  • Context Retrieval — Queries Snipara for relevant documentation per sub-task
  • Sandboxed Execution — Runs generated code in isolated environments
  • Trajectory Logging — Full execution traces for debugging

Configuration

Create an rlm.toml in your project:

[rlm]
model = "claude-sonnet-4-20250514"
environment = "local"  # or "docker"
# Snipara integration
snipara_api_key = "rlm_your_key_here"
snipara_project_slug = "your-project"

Next Steps