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.

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. Hit token limits
4. Give incomplete answer

Get Started in 2 Minutes

The Problem You Have Right Now

Your AI assistant searches files one-by-one using grep/find. With large codebases:

• ❌ Queries take 20+ seconds
• ❌ AI reads 50K tokens to answer simple questions
• ❌ You manually search 5 repos to find "how we do X"
• ❌ AI forgets your preferences next session

The Solution (30 seconds from now)

# 1. Install
pip install snipara-mcp   # Python
npm install snipara-mcp   # Node.js

# 2. Get your API key
# Sign up at https://snipara.com (Free: 100 queries/month)

# 3. Set environment variable
export SNIPARA_API_KEY="your-key-here"

# 4. Add to your MCP client (Claude Code, Cline, Roo Code, etc.)
# Done! Start using rlm_ask() in your next chat

Your First Query (Try This Now)

You: "How does authentication work in my codebase?"