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.

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: 1,000 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?"

Behind the scenes:
  rlm_context_query("authentication")
  → 2 seconds later
  → Returns top 3 relevant docs (3K tokens instead of 50K)

Result: Instant, accurate answer

Note: Before querying, index your docs at https://snipara.com/dashboard (upload .md/.txt/.mdx files).

Core Capabilities (Pick What You Need)

🎯 Quick Answers (Start Here)

Plan Required: ✅ FREE (100 queries/mo)

Tool: rlm_ask Use when: You need a fast answer from your docs Example: rlm_ask("API rate limits") Time saved: 20 seconds → 2 seconds per query

{ "query": "How do we handle webhooks?" }

🔍 Deep Research (Complex Questions)

Plan Required: ✅ FREE (keyword only) | 🔥 PRO ($19/mo for semantic)

Tool: rlm_context_query Use when: You need semantic search with precise token control Example: Find conceptually related content, not just keyword matches Benefit: 90% context reduction (500K → 5K tokens)

{
  "query": "authentication implementation",
  "max_tokens": 6000,
  "search_mode": "hybrid"
}

Search modes by plan:

• keyword - Fast term matching ✅ FREE
• semantic - Embedding similarity 🔥 PRO+
• hybrid - Best of both worlds 🔥 PRO+

🌐 Multi-Repo Search

Plan Required: 👥 TEAM ($49/mo) or ENTERPRISE

Tool: rlm_multi_project_query Use when: You have 5+ repos and don't know which has the answer Example: One query searches ALL your team's projects Time saved: 5 minutes of manual searching → 3 seconds

{
  "query": "Where do we send email notifications?",
  "project_ids": [],
  "max_tokens": 8000
}

⚠️ Not available on Free/Pro plans - Requires Team plan for multi-project access.

🧠 AI Memory (Remember Preferences)

Plan Required: 🔥 PRO ($39/mo Agents) or 👥 TEAM ($79/mo Agents)

Tools: rlm_remember + rlm_recall Use when: You want AI to remember your coding style/decisions Benefit: Consistent code across sessions

Store a memory:

{
  "content": "User prefers TypeScript strict mode with functional components",
  "type": "preference",
  "scope": "project"
}

Recall later:

{
  "query": "What are my coding preferences?",
  "limit": 5
}

Memory types: fact, decision, learning, preference, todo, context

⚠️ Requires separate Agents plan - Memory is part of Agents features, not Context plans.

👥 Team Standards (Auto-Enforce Rules)

Plan Required: 👥 TEAM ($49/mo) or ENTERPRISE

Tool: rlm_shared_context Use when: Your team needs consistent coding practices Setup once: Upload coding standards to Shared Collection Every dev gets: Auto-injected team rules in every query

{
  "categories": ["MANDATORY", "BEST_PRACTICES"],
  "max_tokens": 4000
}

Categories by priority:

• MANDATORY - Non-negotiable rules (security, architecture)
• BEST_PRACTICES - Recommended patterns (40% token budget)
• GUIDELINES - Helpful suggestions
• REFERENCE - Background info

⚠️ Not available on Free/Pro plans - Team-wide features require Team plan.

🔧 Power User Tools

Multi-Query (Parallel Searches):

{
  "queries": [
    { "query": "auth flow", "max_tokens": 3000 },
    { "query": "session management", "max_tokens": 3000 }
  ]
}

Decompose (Break Down Complex Questions):

{ "query": "Explain the complete payment system architecture" }

Plan (Preview Execution):

{ "query": "Find all API endpoints", "strategy": "relevance_first" }

Search (Regex Pattern Matching):

{ "pattern": "async def|async function", "max_results": 20 }

Session Context (Inject Standards):

{ "context": "Use Python 3.11+, prefer dataclasses over Pydantic" }

📄 Document Management

Upload Single Doc:

{ "path": "docs/api.md", "content": "# API Documentation..." }

Bulk Sync (CI/CD Integration):

{
  "documents": [
    { "path": "docs/auth.md", "content": "..." },
    { "path": "docs/api.md", "content": "..." }
  ],
  "delete_missing": false
}

Check Stats:

{}

ROI Calculator

Scenario 1: Solo Developer (Large Codebase)

Current pain: Grep/find searches take 20+ seconds, read 50K tokens per query

Plan recommendation: Start with FREE (100 queries), upgrade to PRO ($19/mo) if you need semantic search.

Scenario 2: Team (5+ Repositories)

Current pain: Switch between 5 projects manually, 5 minutes per search

Plan recommendation: TEAM ($49/mo) for rlm_multi_project_query + shared standards.

Scenario 3: Enterprise (Consistent Standards)

Current pain: 10 devs ask "how do we do X?" daily, inconsistent code

Cost: $49/mo Team or $499/mo Enterprise ROI: Consistency + faster 20+ hours/month saved

Quick Start by Use Case

Use Case 1: "I have huge docs and grep is slow"

Plan: ✅ FREE (100 queries/mo)

# 1. Index your docs once
Visit https://snipara.com/dashboard → Create project → Upload .md/.txt files

# 2. Query instantly
rlm_ask("How does authentication work?")

Use Case 2: "I work on 10 microservices"

Plan: 👥 TEAM ($49/mo)

# 1. Create 10 projects on Snipara dashboard
# 2. Enable Team plan

# 3. Query all repos at once
rlm_multi_project_query("How do we handle rate limiting?")

⚠️ Requires Team plan - Multi-project search not available on Free/Pro.

Use Case 3: "AI keeps forgetting my preferences"

Plan: 🔥 PRO Agents ($39/mo) or 👥 TEAM Agents ($79/mo)

# 1. Enable Agents plan (separate from Context plan)

# 2. Store your preferences once
rlm_remember(type="preference", content="Use functional React components")

# 3. AI recalls them forever
rlm_recall("my coding preferences")

⚠️ Requires separate Agents subscription - Memory features not included in Context plans.

Pricing (Two Subscription Types)

Context Plans (Documentation Search)

Agents Plans (Memory & Swarms)

⚠️ Two separate subscriptions: Context plans for search, Agents plans for memory/swarms.

Try free first: 100 queries is ~5 days of usage to test value.

Example Workflows

Example 1: Quick Answer (FREE plan)

User: "What are our API rate limits?"

You call: rlm_ask("API rate limits")

Result: Returns relevant docs in 2 seconds

Example 2: Semantic Search (PRO plan)

User: "How do we validate user input?"

You call: rlm_context_query("user input validation", search_mode="semantic")

Result: Finds docs about "sanitization", "XSS prevention", "schema validation"
        even if they don't contain exact keywords

Example 3: Multi-Repo Search (TEAM plan)

User: "Show me all webhook implementations across our projects"

You call: rlm_multi_project_query("webhook implementation")

Result: Returns implementations from all 10 microservices in 3 seconds

Example 4: Persistent Memory (PRO Agents plan)

Session 1 (Monday):
  User: "I prefer TypeScript strict mode and functional components"
  You call: rlm_remember(type="preference", content="Prefers TS strict + functional")

Session 2 (Friday - NEW SESSION):
  User: "Create a new React component"
  You call: rlm_recall("coding preferences")
  Result: AI remembers to use functional components from Monday!

Example 5: Team Standards (TEAM plan)

Setup (Admin does once):
  - Upload coding standards to Shared Context Collection
  - Link collection to all team projects

Every developer:
  User: "Write a new API endpoint"
  You call: rlm_shared_context(categories=["MANDATORY"])
  Result: Auto-injects team's API design rules, security requirements, etc.

Support & Resources

• Website: https://snipara.com
• Documentation: https://docs.snipara.com
• GitHub: https://github.com/snipara/snipara-mcp
• Issues: https://github.com/snipara/snipara-mcp/issues
• Email: support@starbox-group.com

Quick Tips

1. Start small: Use rlm_ask for quick answers on FREE plan
2. Upgrade smart: Get PRO when keyword search isn't finding what you need
3. Team value: Multi-project search pays for itself with 5+ repos
4. Memory requires separate plan: Context + Agents are two subscriptions
5. Index first: Upload docs to dashboard before querying

When in doubt, start with FREE and upgrade based on value received. 🚀

Complete Tool Reference (For Power Users)

Query Tools (All Plans)

rlm_ask - Quick keyword search

{ "query": "API rate limits" }

rlm_context_query - Full-featured semantic search

{
  "query": "authentication",
  "max_tokens": 6000,
  "search_mode": "hybrid",
  "include_metadata": true
}

rlm_search - Regex pattern search

{
  "pattern": "async def|async function",
  "max_results": 20
}

rlm_inject - Set session context

{
  "context": "Use Python 3.11+, prefer dataclasses",
  "append": false
}

rlm_context - Show current session context

{}

rlm_clear_context - Clear session context

{}

Advanced Query Tools (Pro+)

rlm_multi_query - Parallel queries

{
  "queries": [
    { "query": "auth flow", "max_tokens": 3000 },
    { "query": "session management", "max_tokens": 3000 }
  ],
  "max_tokens": 8000
}

rlm_decompose - Break down complex questions

{
  "query": "Explain payment system architecture",
  "max_depth": 2
}

rlm_plan - Generate execution plan

{
  "query": "Find all API endpoints",
  "strategy": "relevance_first",
  "max_tokens": 16000
}

Team Tools (Team+ Plan)

rlm_multi_project_query - Search across all repos

{
  "query": "webhook implementation",
  "project_ids": [],
  "exclude_project_ids": [],
  "max_tokens": 8000,
  "per_project_limit": 3
}

rlm_shared_context - Get team standards

{
  "categories": ["MANDATORY", "BEST_PRACTICES"],
  "max_tokens": 4000,
  "include_content": true
}

rlm_list_templates - Browse prompt templates

{
  "category": "code-review"
}

rlm_get_template - Use template with variables

{
  "slug": "security-review",
  "variables": {
    "author": "John",
    "pr_number": "123"
  }
}

rlm_list_collections - List shared collections

{
  "include_public": true
}

rlm_upload_shared_document - Upload to shared collection

{
  "collection_id": "col_abc123",
  "title": "TypeScript Standards",
  "content": "# Standards...",
  "category": "BEST_PRACTICES",
  "priority": 90
}

Memory Tools (Agents Plan)

rlm_remember - Store memory

{
  "content": "User prefers functional components",
  "type": "preference",
  "scope": "project",
  "category": "coding-style",
  "ttl_days": null
}

rlm_recall - Query memories

{
  "query": "What are my preferences?",
  "type": "preference",
  "limit": 5,
  "min_relevance": 0.5
}

rlm_memories - List all memories

{
  "type": "preference",
  "category": "coding-style",
  "limit": 20,
  "offset": 0
}

rlm_forget - Delete memories

{
  "memory_id": "mem_abc123"
}

Document Management Tools

rlm_upload_document - Upload single doc

{
  "path": "docs/api.md",
  "content": "# API Documentation..."
}

rlm_sync_documents - Bulk upload

{
  "documents": [
    { "path": "docs/auth.md", "content": "..." },
    { "path": "docs/api.md", "content": "..." }
  ],
  "delete_missing": false
}

rlm_store_summary - Store document summary

{
  "document_path": "docs/api.md",
  "summary": "RESTful API with OAuth2 auth...",
  "summary_type": "concise",
  "generated_by": "claude-3.5-sonnet"
}

rlm_get_summaries - Get stored summaries

{
  "document_path": "docs/api.md",
  "summary_type": "concise"
}

rlm_stats - Get documentation stats

{}

rlm_sections - List indexed sections

{
  "filter": "auth",
  "limit": 50,
  "offset": 0
}

rlm_read - Read specific lines

{
  "start_line": 1,
  "end_line": 100
}

Advanced Features (Enterprise)

rlm_swarm_create - Create agent swarm

{
  "name": "code-review-swarm",
  "description": "Parallel code review",
  "max_agents": 10
}

rlm_swarm_join - Join swarm

{
  "swarm_id": "swarm_abc123",
  "agent_id": "agent_1",
  "role": "worker",
  "capabilities": ["review", "test"]
}

rlm_claim - Claim resource for exclusive access

{
  "swarm_id": "swarm_abc123",
  "agent_id": "agent_1",
  "resource_type": "file",
  "resource_id": "src/auth.ts",
  "timeout_seconds": 300
}

rlm_release - Release claimed resource

{
  "swarm_id": "swarm_abc123",
  "agent_id": "agent_1",
  "claim_id": "claim_abc123"
}

rlm_state_get - Read swarm state

{
  "swarm_id": "swarm_abc123",
  "key": "progress"
}

rlm_state_set - Write swarm state

{
  "swarm_id": "swarm_abc123",
  "agent_id": "agent_1",
  "key": "progress",
  "value": { "completed": 5, "total": 10 },
  "expected_version": 1
}

rlm_broadcast - Broadcast event to swarm

{
  "swarm_id": "swarm_abc123",
  "agent_id": "agent_1",
  "event_type": "task_completed",
  "payload": { "task_id": "task_1" }
}

rlm_task_create - Create swarm task

{
  "swarm_id": "swarm_abc123",
  "agent_id": "agent_1",
  "title": "Review auth module",
  "description": "Security review",
  "priority": 90
}

rlm_task_claim - Claim task from queue

{
  "swarm_id": "swarm_abc123",
  "agent_id": "agent_1",
  "task_id": "task_abc123"
}

rlm_task_complete - Mark task complete

{
  "swarm_id": "swarm_abc123",
  "agent_id": "agent_1",
  "task_id": "task_abc123",
  "success": true,
  "result": { "issues_found": 0 }
}

Settings & Configuration

rlm_settings - Get project settings

{
  "refresh": false
}

Returns current project configuration including:

• Max tokens per query
• Default search mode
• Rate limits
• Enabled features

For complete API documentation, visit: https://docs.snipara.com