MCP Server Overview

What is MCP?

Model Context Protocol (MCP) is Solatis's centralized AI orchestration layer that manages interactions with multiple AI models, handles context, and optimizes costs.

Architecture

┌────────────────────────────────────────┐
│         Applications                   │
│  (Web, Mobile, API Clients)           │
└─────────────┬──────────────────────────┘
              │
┌─────────────▼──────────────────────────┐
│         MCP Server                      │
│  - Request routing                      │
│  - Context management                   │
│  - Model selection                      │
│  - Cost optimization                    │
│  - Rate limiting                        │
└─────────────┬──────────────────────────┘
              │
       ┌──────┴──────┬──────────┐
       │             │          │
┌──────▼─────┐ ┌────▼────┐ ┌──▼──────┐
│  OpenAI    │ │ Anthropic│ │  Local  │
│  GPT-4     │ │  Claude  │ │  Models │
└────────────┘ └──────────┘ └─────────┘

Key Features

Model Routing

Intelligent Selection:

// MCP automatically selects best model
const response = await mcp.complete({
  prompt: "Summarize this document",
  context: documentText,
  // MCP chooses: GPT-4 for quality, Claude for long docs, etc.
});

Routing Logic:

Short query (< 500 tokens) → GPT-4-mini (fast, cheap)
Long document (> 8K tokens) → Claude (large context)
Code generation → GPT-4 (best for code)
Creative writing → Claude (nuanced)
Cost-sensitive → GPT-3.5 (cheapest)

Context Management

Automatic Context Window:

const conversation = mcp.createConversation({
  maxTokens: 128000,  // Claude's limit
  documents: [doc1, doc2, doc3],
  history: previousMessages
});

// MCP manages context automatically
await conversation.ask("What are the key points?");
await conversation.ask("Compare doc1 and doc2");
// Context preserved across calls

Context Strategies:

• Sliding window (keep recent)
• Summarization (compress old)
• Hierarchical (detailed recent, summarized old)
• Semantic selection (keep relevant)

Cost Optimization

Automatic Optimization:

// Dev/test: Use cheaper models
if (env === 'development') {
  mcp.setDefaultModel('gpt-3.5-turbo');
}

// Production: Balance cost and quality
mcp.setStrategy({
  model: 'adaptive',  // Auto-select
  maxCostPerRequest: 0.10,  // Budget limit
  fallback: 'gpt-3.5-turbo'  // If over budget
});

Cost Tracking:

Request ID: req_abc123
Model: gpt-4
Input tokens: 1,247
Output tokens: 456
Cost: $0.0523

Monthly Usage:
Total requests: 15,234
Total cost: $156.78
Average: $0.0103/request

Rate Limiting

Per-Model Limits:

OpenAI GPT-4: 10,000 TPM (tokens per minute)
Anthropic Claude: 100,000 TPM
Local models: Unlimited

MCP queues overflow requests
Retry with exponential backoff
Fallback to alternative models

API Examples

Basic Usage

import { MCPClient } from '@solatis/mcp';

const mcp = new MCPClient({
  apiKey: process.env.SOLATIS_API_KEY
});

// Simple completion
const response = await mcp.complete({
  prompt: "Explain quantum computing",
  maxTokens: 500
});

console.log(response.text);

With Context

// Chat with documents
const chat = await mcp.chat({
  messages: [
    { role: 'user', content: 'What are the revenue numbers?' }
  ],
  context: {
    documents: ['q3-report-uuid'],
    workspace: 'finance-workspace'
  }
});

Streaming

// Stream response for better UX
const stream = await mcp.complete({
  prompt: "Write a long article about AI",
  stream: true
});

for await (const chunk of stream) {
  process.stdout.write(chunk.text);
}

Batch Processing

// Process multiple items efficiently
const results = await mcp.batch({
  requests: documents.map(doc => ({
    operation: 'summarize',
    input: doc.content,
    options: { maxLength: 200 }
  })),
  parallelism: 5  // Process 5 at a time
});

Configuration

Server Setup

# mcp.config.yaml
server:
  port: 8080
  host: 0.0.0.0
  
models:
  - name: gpt-4
    provider: openai
    apiKey: ${OPENAI_API_KEY}
    rateLimit: 10000  # TPM
    priority: high
    
  - name: claude-3-opus
    provider: anthropic
    apiKey: ${ANTHROPIC_API_KEY}
    rateLimit: 100000
    priority: medium
    
  - name: gpt-3.5-turbo
    provider: openai
    apiKey: ${OPENAI_API_KEY}
    rateLimit: 90000
    priority: low
    costMultiplier: 0.1  # 10x cheaper than GPT-4
    
routing:
  strategy: adaptive
  factors:
    - quality: 0.5
    - cost: 0.3
    - latency: 0.2
    
cache:
  enabled: true
  ttl: 3600  # seconds
  maxSize: 1GB
  
monitoring:
  enabled: true
  metrics: prometheus
  logging: structured

Monitoring

Metrics

// Request metrics
{
  requestId: 'req_123',
  model: 'gpt-4',
  latency: 1234,  // ms
  tokens: {
    input: 500,
    output: 200,
    total: 700
  },
  cost: 0.035,
  cache: 'miss',
  status: 'success'
}

// Aggregate metrics
{
  period: '1h',
  requests: 156,
  avgLatency: 892,
  p95Latency: 1567,
  totalCost: 5.45,
  cacheHitRate: 0.23,
  errorRate: 0.02
}

Dashboard

Key Metrics:

• Requests per second
• Average latency
• Cost per hour/day/month
• Model distribution
• Error rate
• Cache hit rate
• Queue depth

Best Practices

Efficiency:

• Cache frequent queries
• Batch similar requests
• Use cheaper models for drafts
• Stream for long responses
• Set appropriate timeouts

Reliability:

• Implement retry logic
• Handle rate limits gracefully
• Use fallback models
• Monitor error rates
• Log all requests

Cost Control:

• Set budget limits
• Monitor usage daily
• Use tiered models
• Optimize prompts
• Cache aggressively

Troubleshooting

High Costs:

• Review usage patterns
• Optimize prompts (shorter)
• Use cheaper models
• Increase caching
• Set budget alerts

Slow Responses:

• Check model selection
• Review prompt length
• Use streaming
• Optimize context
• Increase parallelism

Rate Limits:

• Implement queuing
• Add retry logic
• Use multiple API keys
• Request limit increase
• Fallback to alternatives

Next Steps

• API Authentication - API security
• Data Flow - System architecture
• Building Agents - AI automation

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Last Updated: October 11, 2025