What is Model Context Protocol (MCP)?

MCP is a standard protocol that allows AI assistants to securely connect to external data sources and tools. It enables AI to access your specific business data and metrics rather than relying solely on general training data, reducing hallucinations and increasing accuracy.

How does MCP reduce AI hallucinations?

By providing AI with direct access to verified business context - metric definitions, actual data, business rules - MCP eliminates the need for AI to guess. Instead of generating plausible-sounding answers, AI can retrieve accurate information from authoritative sources.

Is MCP secure for enterprise data?

Yes. MCP is designed with enterprise security in mind. It supports authentication, authorization, and audit logging. The AI only accesses data the user is permitted to see, and all access is logged. Data does not need to be sent to AI training.

Which AI assistants support MCP?

MCP support is growing across AI platforms. Claude, developed by Anthropic, has native MCP support. Other AI systems are adding support as the protocol gains adoption. Check with your AI vendor for current compatibility.

Model Context Protocol (MCP): Connecting AI to Your Business Data

Model Context Protocol (MCP) is an open standard that enables AI assistants to connect securely to external data sources, tools, and business knowledge. For enterprise analytics, MCP bridges the gap between general-purpose AI and organization-specific data.

This connection is what transforms AI from a tool that guesses about your business to one that knows your business.

The Problem MCP Solves

AI Without Context

General-purpose AI assistants have a fundamental limitation:

They do not know your business:

They do not know your metric definitions
They cannot access your actual data
They do not understand your business rules
They cannot verify their answers against your truth

This lack of context causes hallucinations - plausible-sounding but incorrect responses.

The Context Gap

When a user asks "What was our revenue last quarter?":

Without MCP: AI must either refuse to answer or generate a made-up number With MCP: AI can query your actual data through your semantic layer and return accurate results

MCP provides the bridge that closes this context gap.

How MCP Works

Protocol Architecture

MCP defines a standard way for AI to interact with external systems:

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│  AI Assistant   │────▶│   MCP Server    │────▶│  Data Sources   │
│  (e.g., Claude) │◀────│  (e.g., Codd)   │◀────│  (Your Data)    │
└─────────────────┘     └─────────────────┘     └─────────────────┘

AI Assistant: The conversational interface users interact with MCP Server: Translates AI requests into data operations Data Sources: Your actual business data and definitions

Key Capabilities

MCP enables several types of interactions:

Resources: Access to data, documents, and context

Metric definitions and documentation
Query results from your data
Business context and rules

Tools: Actions the AI can take

Execute analytics queries
Look up specific metrics
Retrieve historical data

Prompts: Structured templates for common operations

Standard analytics workflows
Consistent question handling
Domain-specific interactions

Security Model

MCP is designed for enterprise security requirements:

Authentication: Users must be authenticated Authorization: Access limited to permitted data Audit logging: All operations are recorded Data isolation: Your data stays in your systems

The AI accesses data on behalf of authenticated users, respecting existing permissions.

MCP for Analytics

Semantic Layer Integration

MCP connects AI directly to semantic layers:

User: "What was enterprise revenue last quarter?"
           │
           ▼
┌─────────────────────────────────────────────────┐
│  AI interprets question, calls MCP             │
│  → get_metric("enterprise_revenue", "Q3 2024") │
└────────────────────────┬────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────┐
│  MCP Server (Codd AI) processes request        │
│  → Validates metric exists                     │
│  → Checks user authorization                   │
│  → Queries semantic layer                      │
│  → Returns verified result                     │
└────────────────────────┬────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────┐
│  AI formats response with actual data          │
│  "Enterprise revenue in Q3 2024 was $8.1M"    │
└─────────────────────────────────────────────────┘

The AI uses verified metric definitions and actual data - no guessing required.

Context Provision

Beyond queries, MCP provides context that improves AI understanding:

Metric context:

{
  "metric": "enterprise_revenue",
  "definition": "Revenue from accounts with >$100K ACV",
  "calculation": "SUM(revenue) WHERE segment='enterprise'",
  "owner": "Finance",
  "last_updated": "2024-05-01"
}

Relationship context:

{
  "entity": "customer",
  "related_entities": ["orders", "subscriptions", "support_tickets"],
  "common_analyses": ["lifetime value", "churn risk", "expansion potential"]
}

This context helps AI interpret questions correctly.

Query Execution

MCP enables AI to execute queries against your data:

AI interprets user question
AI calls MCP to execute appropriate query
MCP validates and executes through semantic layer
Results return to AI
AI formats response for user

The semantic layer ensures query accuracy; MCP ensures secure access.

Implementing MCP

Server Setup

MCP servers expose your data to AI assistants:

Option 1: Purpose-built platforms Platforms like Codd AI provide MCP servers that connect to your semantic layer, handling the complexity of secure AI integration.

Option 2: Custom development Organizations can build MCP servers for specialized needs, following the protocol specification.

Client Configuration

AI assistants connect to MCP servers through configuration:

{
  "mcpServers": {
    "codd-analytics": {
      "url": "https://your-instance.codd.ai/mcp",
      "authentication": {
        "type": "oauth2",
        "provider": "your-identity-provider"
      }
    }
  }
}

Once configured, the AI can access analytics capabilities.

Capability Definition

MCP servers define what capabilities are available:

Resources (what AI can read):

Metric catalog
Documentation
Query results

Tools (what AI can do):

Execute analytics queries
Look up definitions
Retrieve historical data

Scope capabilities appropriately for your security requirements.

Benefits of MCP for Analytics

Accuracy

AI answers come from verified sources:

Certified metric definitions
Actual data from your systems
Business rules correctly applied

No hallucination - just facts from your data.

Consistency

Everyone gets the same answers:

Same definitions across all AI interactions
Same data regardless of who asks
Same business rules always applied

MCP ensures the AI uses your single source of truth.

Security

Enterprise requirements are met:

Existing authentication integrated
Row-level security respected
All access audited
Data stays in your control

Flexibility

Use AI where you work:

Access analytics from any MCP-enabled AI
Same capabilities in different interfaces
Evolve AI tools without changing data layer

MCP vs Alternative Approaches

vs RAG (Retrieval Augmented Generation)

RAG retrieves document chunks for context:

Aspect	RAG	MCP
Data type	Documents, text	Structured data, APIs
Precision	Approximate retrieval	Exact queries
Security	Document-level	Field-level possible
Real-time	Depends on indexing	Direct data access

MCP complements RAG for different data types.

vs Fine-Tuning

Fine-tuning bakes knowledge into models:

Aspect	Fine-Tuning	MCP
Updates	Requires retraining	Immediate
Specificity	General patterns	Exact answers
Data security	Training data exposure	Runtime access
Maintenance	Model management	API management

MCP provides current data; fine-tuning provides capabilities.

vs Custom Integrations

Custom integrations can achieve similar results:

Aspect	Custom	MCP
Development	Build from scratch	Standard protocol
Maintenance	Ongoing custom work	Protocol updates
Compatibility	Specific to one AI	Multi-AI support
Security	Custom implementation	Established patterns

MCP reduces integration effort through standardization.

The Codd AI MCP Integration

Codd AI provides MCP server capabilities that connect your semantic layer to AI assistants:

What Codd provides:

MCP server implementation
Semantic layer connection
Security and governance integration
Query execution and validation

What you gain:

Accurate AI analytics in any MCP-enabled assistant
Consistent metrics across all AI interactions
Enterprise security maintained
Single source of truth enforced

This integration lets you bring your organization's analytics capabilities into whatever AI tools your team prefers.

Getting Started

Assess Readiness

Before implementing MCP:

Semantic layer with metric definitions
Authentication infrastructure
Clear access policies
Governance processes

Start Small

Begin with limited scope:

Single domain or use case
Controlled user group
High-value, well-defined metrics

Expand Based on Success

Grow MCP usage as it proves value:

Additional metrics and domains
More users and use cases
Additional AI platforms

MCP represents the future of AI-data integration - a standardized, secure way to give AI the context it needs to be genuinely useful for business decisions.