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. Hit token limits
4. Give incomplete answer

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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?"

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).

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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+

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🌐 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.

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🧠 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:

{}

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ROI Calculator

Scenario 1: Solo Developer (Large Codebase)

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

Metric	Before Snipara	With Snipara	Savings
Query speed	20 seconds	2 seconds	18 seconds
Daily queries	50	50	-
Time per day	16 minutes	1.6 minutes	14.4 min/day
Time per month	7.2 hours	0.72 hours	6.5 hours/month
Cost	$0	$0-19/mo	ROI: 6.5 hours saved

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

Metric	Before Snipara	With Snipara	Savings
Multi-repo search	5 min	3 seconds	4.97 min
Searches per day	10	10	-
Time per day	50 minutes	30 seconds	49.5 min/day
Time per month	24.75 hours	0.25 hours	24.5 hours/month
Cost	$0	$49/mo Team	ROI: 24.5 hours saved

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

Before	With Snipara Shared Context
❌ Each dev googles/asks Slack	✅ Standards auto-injected in every query
❌ Inconsistent patterns	✅ Enforced team conventions
❌ Onboarding takes 2 weeks	✅ New devs get standards instantly
❌ Code review conflicts	✅ Code follows standards from day 1

Cost: $49/mo Team or $499/mo Enterprise ROI: Consistency + faster onboarding = easily 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)

Plan	Price	Queries/mo	Search Mode	Multi-Project
FREE	$0	100	Keyword only	❌
PRO	$19/mo	5,000	Semantic + Hybrid	❌
TEAM	$49/mo	20,000	Semantic + Hybrid	✅
ENTERPRISE	$499/mo	Unlimited	Semantic + Hybrid	✅

Agents Plans (Memory & Swarms)

Plan	Price	Prerequisite	Features
STARTER	$15/mo	None	Basic memory (100 memories)
PRO	$39/mo	None	Unlimited memories, swarms
TEAM	$79/mo	Context TEAM+	Team-wide memory sharing
ENTERPRISE	$199/mo	Context ENTERPRISE	Advanced coordination

⚠️ 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.

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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.

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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@snipara.com

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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. 🚀

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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 }
}

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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

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