MCP Integration - Connect AI to Your Data and Tools

Core Value

MCP (Model Context Protocol) is an open standard introduced by Anthropic that enables AI to securely access enterprise tools and data sources. atypica.AI's native MCP support helps teams:

Break Data Silos: AI directly accesses internal data without manual exports
Unlimited Extensions: Connect existing team tools without rebuilding
Secure and Controlled: Data stays within team infrastructure with secure access

Simple Explanation:

Traditional AI: Limited to public web data and preset functions
MCP Integration: AI can invoke your team's proprietary tools (databases, internal systems, custom APIs)

Overview: With vs Without MCP Integration

Scenario	Without MCP Integration	With MCP Integration
Data Sources	Public web data only	Internal databases, Jupyter Notebooks, private APIs
Tool Extension	atypica built-in tools only	Custom team tools
Research Depth	Surface-level (public info)	Deep-dive (internal + public data)
Team Customization	Generic research workflows	Team-specific research workflows
Data Security	Data leaves team boundaries	Data stays within team infrastructure (via internal MCP server)

Real-World Comparison

Scenario: Analyze user behavior data

Without MCP Integration (30 minutes):

User: "Analyze our product's user behavior data"AI: Cannot access internal data   → User must manually export data   → User copies/pastes data into chat (limited by character length)   → AI analyzes incomplete dataResult: Superficial analysis, incomplete data

With MCP Integration (5 minutes):

User: "Analyze our product's user behavior data"AI: Invokes team's JupyterDataMCP   → Directly accesses Jupyter Notebook analysis results   → Automatically retrieves latest data and visualizations   → Performs deep analysis on complete datasetResult: In-depth analysis, complete data, fully automated

Efficiency Gains:

Time: 30 minutes → 5 minutes (83% reduction)
Data Completeness: Incomplete → Complete (100%)
Automation: Manual → Automated

How MCP Works

Simple Understanding

Model Context Protocol (MCP) is an open standard introduced by Anthropic that defines how AI securely accesses external tools and data.

Connection Flow:

Your Question → atypica.AI → Team's MCP Server → Internal Data/Tools

Core Features:

Unified Standard: Follows MCP open standard, compatible with ecosystem
Secure Isolation: Each team independently configured, data segregated
Flexible Integration: Supports various data sources and tools

Team-Level Configuration

Flexible Setup, Secure Access

Each team can configure their own MCP servers in the admin dashboard without requiring technical implementation:

Configuration Content:

Server Address: Access URL for the team's MCP server
Authentication: API Key or access token
Function Description: Tell AI what this tool can do

Example Scenarios:

Configure Jupyter analysis tool to let AI access team's data analysis results
Configure internal database query tool to let AI directly query user behavior data
Configure internal API tool to let AI invoke team's proprietary services

Key Features:

Instant Activation: Takes effect immediately across all research sessions
Team Isolation: Each team's configuration is independent, data securely segregated
Flexible Updates: Add, modify, or remove MCP server configurations anytime

Built-in Deep Research Tool

atypica.AI provides a built-in DeepResearch MCP server for teams to quickly experience MCP integration capabilities.

Core Capabilities

DeepResearch is a deep research tool that executes complex multi-step research tasks:

Key Features:

Deep Analysis: Integrated with Grok model for in-depth insights
Real-time Feedback: Live progress updates during research
Secure Access: Supports API Key authentication

Usage: Teams can configure DeepResearch as an MCP tool for AI to invoke automatically during research

Use Case Examples

Scenario 1: Access Jupyter Data Analysis

Business Need: Team's data analysts store research results in Jupyter Notebooks, and want AI to directly reference these analysis outputs.

Solution: Deploy a Jupyter MCP server to let AI directly read analysis results, charts, and conclusions from Notebooks.

Configuration: Configure Jupyter MCP server address and access credentials in team admin dashboard.

Real Usage:

User (on atypica.AI): "Analyze user retention rate, reference notebooks/retention_analysis.ipynb"AI automatically invokes query_notebook tool:  → Parameters: {"notebook_path": "notebooks/retention_analysis.ipynb"}  → Retrieves Notebook analysis results  → Answers user question based on resultsAI Response:"Based on your retention_analysis.ipynb Notebook, here's the user retention analysis:- Day 1 retention: 45%- Day 7 retention: 28%- Day 30 retention: 15%Key Findings:1. Day 1 → Day 7 churn rate reaches 38% (onboarding needs optimization)2. Paid user retention is 2.3x that of free users3. Users engaging with core features show 60% higher retentionRecommendations:1. Optimize onboarding flow2. Guide users to core features3. Design paid conversion paths"

Scenario 2: Query Internal Database

Business Need: Business teams need quick access to user behavior data, transaction data, and other internal data for analysis.

Solution: Deploy a database MCP server to let AI securely query internal databases (read-only queries supported).

Configuration: Configure database MCP server address and access credentials in team admin dashboard.

Real Usage:

User: "Query active users in the last 7 days"AI automatically invokes query_user_behavior tool:  → Parameters: {      "sql": "SELECT COUNT(DISTINCT user_id) as active_users FROM user_events WHERE created_at >= NOW() - INTERVAL '7 days'"    }  → Retrieves query results  → Responds based on resultsAI Response:"Active users in the last 7 days: 12,458.Compared to last week:- Increased 8.5% (from 11,487 last week)- Growth primarily from mobile (+12%)- Desktop slightly declined (-2%)Recommend focusing on mobile user experience optimization."

How AI Intelligently Invokes Tools

Automatic Recognition and Invocation

When AI starts research, it automatically recognizes all available tools:

Tool Recognition:

AI can see all MCP tools configured by the team
Each tool has clear functional descriptions
AI automatically selects appropriate tools based on user needs

Intelligent Decision:

User: "Analyze user retention rate"AI Automatic Reasoning:1. Identifies need for internal user data2. Discovers team has database query tool configured3. Automatically invokes that tool to fetch data4. Provides analysis based on real data

Key Advantages:

No need for users to manually specify tools
AI automatically selects the most suitable data source
Team members simply ask questions naturally

Capability Boundaries

✅ What MCP Integration Can Do

1. Data Source Integration

✅ Jupyter Notebooks
✅ Internal databases (PostgreSQL, MySQL, MongoDB)
✅ Internal APIs
✅ File systems
✅ Cloud storage (S3, GCS)

2. Tool Integration

✅ Data analysis tools
✅ Visualization tools
✅ Machine learning models
✅ External APIs (Twitter, Reddit, etc.)

3. Workflow Integration

✅ CI/CD systems
✅ Project management tools (Jira, Linear)
✅ Documentation systems (Notion, Confluence)

4. Security Features

✅ Team-level isolation (each team independently configured)
✅ Authentication mechanisms (API Key, Header auth)
✅ Data stays within team infrastructure (via internal MCP Server)

❌ What MCP Integration Cannot Do

1. Cross-Team Access

❌ Team A cannot access Team B's MCP Server
❌ Personal users cannot use team MCP (requires team ID)

2. Real-Time Data Streaming

❌ No WebSocket persistent connections
✅ Supports SSE unidirectional streaming output

3. File Uploads

❌ MCP tools cannot directly upload files to atypica
✅ Can return file content (text/JSON)

4. Long-Running Tasks

❌ MCP tool calls have timeout limits (typically 2-5 minutes)
✅ Can solve via async tasks + status polling

Configuration Recommendations

Tool Naming Guidelines

Clear and Meaningful Names:

✅ Good naming: query_user_behavior (query user behavior), get_notebook_results (get notebook results)
❌ Bad naming: tool1 (meaningless), data (too vague)

Function Description Essentials

Clear Function Descriptions:

Explain the tool's purpose and use cases
Describe what data can be retrieved
Note any limitations (e.g., read-only, data volume limits)

Security Recommendations

Permission Control:

Grant only necessary data access permissions
Implement sensitive data masking
Configure access tokens and rotate regularly

Data Security:

Data transmitted through encrypted channels
Team data mutually isolated
Support audit log tracking

Frequently Asked Questions

Q: Why aren't MCP tools being invoked?

Check Points:

Confirm user belongs to a team with MCP configured
Verify configuration is saved correctly
Ensure tool description is clear for AI to understand use case

Q: How to ensure data security?

Security Mechanisms:

All access requires authentication
Data transmission encrypted
Team-level isolation, data segregated
Support read-only permission configuration

Q: What types of tools can be integrated?

Supported Types:

Data analysis tools (Jupyter, data visualization)
Database queries (PostgreSQL, MySQL, MongoDB)
Internal APIs and services
Documentation systems (Notion, Confluence)
Project management tools (Jira, Linear)

Future Plans

Coming Soon

Visual Configuration Interface - Graphical configuration and testing of MCP tools
More Built-in Tools - MCP integration for common tools like Notion, Slack, GitHub
MCP Template Marketplace - One-click deployment of common MCP server configurations

Long-term Vision

Developer Toolkits - Multi-language SDKs to simplify MCP server development
Advanced Permission Management - More granular access control
Usage Analytics - MCP tool invocation statistics and performance monitoring

Summary

MCP Integration is atypica.AI's core differentiator, empowering teams to:

Core Value

Break Data Silos: AI directly accesses internal data without manual exports
Unlimited Tool Extension: Integrate existing team tools without rebuilding
Secure and Controlled: Data stays within team infrastructure via secure MCP Server access
Standard Protocol: Follows MCP standard for future compatibility with more tools

Suitable Use Cases

✅ Suitable for:

Data-driven research (requires internal data access)
Workflow automation (invoking internal tools)
Team-customized research processes
Privacy-sensitive scenarios (data stays within team)

❌ Not suitable for:

Individual users (no team ID)
Pure public data research (MCP not needed)
Simple queries (built-in tools sufficient)

Relationship with Other Features

Plan Mode (Intent Clarification Layer)    ↓Reference Research + File Attachments (Context Loading)    ↓MCP Integration (Tool Extension) ← You are here    ↓Study Agent / Fast Insight Agent (Execution Layer)    ↓Memory System (Persistent Memory)

Feature Synergy:

Plan Mode: Determines whether to invoke MCP tools
File Attachments: Upload external files; MCP accesses internal data (complementary)
Memory System: Remembers MCP tool usage preferences