Snipara Mcp

name: Snipara MCP - Smart Documentation Search
description: Find answers in your codebase 10x faster with semantic search. Query multiple repos at once. AI remembers your preferences across sessions.

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# System Instructions for AI Agents

You are an AI assistant with access to Snipara MCP tools. Use these tools to provide faster, more accurate answers by querying pre-indexed documentation instead of reading files one-by-one.

## When to Use Snipara Tools

✅ **USE Snipara when:**

- User asks "How does X work?" → Use `rlm_ask` or `rlm_context_query`
- User asks "Where do we do X?" → Use `rlm_multi_project_query` (if Team plan)
- User asks "What are my preferences?" → Use `rlm_recall`
- You need to find code patterns across multiple repos
- You need to understand architecture/documentation

❌ **DON'T use Snipara when:**

- User asks you to read a SPECIFIC file they named → Use native Read tool
- User asks you to edit/write code → Use native Edit/Write tools
- User asks to run commands → Use native Bash tool
- Snipara API returns error → Fall back to native tools

## Recommended Workflow

1. **First query:** Always start with `rlm_ask` (fast, works on Free plan)
2. **If not found:** Try `rlm_context_query` with `search_mode: "semantic"` (requires Pro)
3. **Multi-repo search:** Use `rlm_multi_project_query` if user has Team plan
4. **Remember decisions:** Use `rlm_remember` to store important info for future sessions

## Error Handling

If Snipara returns an error:

- Check if user has required plan (Free/Pro/Team)
- Check if docs are indexed (user needs to upload to dashboard first)
- Fall back to native Read/Grep/Glob tools
- Explain to user what went wrong and suggest solution

## Example Usage

```
User: "How does our authentication system work?"

Good response:
1. Call rlm_ask("authentication system")
2. Get relevant docs in 2 seconds
3. Synthesize answer from returned context

Bad response:
1. Use Grep to search for "auth"
2. Read 10 files sequentially
3. Hi...

# Snipara MCP

[![PyPI version](https://badge.fury.io/py/snipara-mcp.svg)](https://pypi.org/project/snipara-mcp/)
[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

**Model Context Protocol (MCP) server for [Snipara](https://snipara.com) - Context optimization and Agent infrastructure for LLMs.**

![Snipara MCP Demo](https://snipara.com/images/mcp-demo.gif)

## Overview

Snipara MCP is a client package that connects AI assistants to the [Snipara API](https://snipara.com), enabling:

- **🔍 90% Context Reduction** - Query 500K tokens, get 5K relevant results
- **🌐 Multi-Project Search** - Search across all your repos in one query
- **🧠 AI Memory** - Persistent semantic memory across sessions
- **👥 Team Standards** - Auto-inject coding standards into every query
- **🤖 Multi-Agent Swarms** - Coordinate multiple AI agents with shared state

### How It Works

```
┌─────────────────────────────────────────────────────────────────┐
│  Your AI Assistant (Claude, GPT, Cursor, etc.)                 │
│  ├── Asks: "How does authentication work?"                     │
│  └── Uses: rlm_context_query("authentication")                 │
└────────────────────────────┬────────────────────────────────────┘
                             │
                   ┌─────────▼─────────┐
                   │  Snipara MCP      │  (This package)
                   │  Client Library   │
                   └─────────┬─────────┘
                             │ HTTPS
                             ▼
                   ┌─────────────────────┐
                   │  Snipara API        │
                   │  api.snipara.com    │
                   │  - Indexed docs     │
                   │  - Semantic search  │
                   │  - Agent memory     │
                   └─────────┬───────────┘
                             │...