AI ML

🔌 MCP (Model Context Protocol)

Connecting AI agents to the tools and data they need

2+ Years Experience
8+ Projects Delivered
Available for new projects

$ cat services.json

MCP Server Development

Build custom MCP servers to expose your tools and data to AI agents.

Deliverables:
  • Tool definition and schemas
  • Resource providers
  • Context management
  • Authentication integration
  • Error handling

AI Agent Tool Integration

Connect AI agents to your existing systems via MCP.

Deliverables:
  • Database access tools
  • API wrappers
  • File system access
  • Custom business logic
  • Security controls

Enterprise MCP Architecture

Design MCP infrastructure for enterprise AI deployments.

Deliverables:
  • Multi-tenant architecture
  • Access control
  • Audit logging
  • Rate limiting
  • Monitoring

$ man mcp-model-context-protocol

What is MCP?

Model Context Protocol (MCP) is a standard for connecting AI models to external tools and data sources. It enables:

  • Tool Calling: AI can execute functions in your systems
  • Resource Access: AI can read from databases, files, APIs
  • Context Sharing: Consistent context across AI interactions
  • Security: Controlled access with proper authentication

MCP vs Function Calling

MCP goes beyond simple function calling:

FeatureFunction CallingMCP
StandardizationProvider-specificUniversal protocol
ResourcesNot supportedFirst-class support
ContextPer-requestPersistent
DiscoveryManualAutomatic
EcosystemFragmentedGrowing standard

$ cat README.md

MCP Server Example

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from mcp import Server, Tool, Resource

server = Server("enterprise-tools")

@server.tool()
async def search_documents(
    query: str,
    filters: dict = None,
    limit: int = 10
) -> list[dict]:
    """Search company documents with semantic understanding."""
    results = await vector_store.similarity_search(
        query=query,
        k=limit,
        filter=filters
    )
    return [
        {"title": r.title, "content": r.content, "source": r.source}
        for r in results
    ]

@server.resource("database://customers/{customer_id}")
async def get_customer(customer_id: str) -> dict:
    """Retrieve customer information by ID."""
    customer = await db.customers.find_one({"id": customer_id})
    return customer

# Run with proper authentication
server.run(auth=enterprise_auth_middleware)

MCP Use Cases

Use CaseTools/ResourcesExample
Knowledge BaseDocument search, Q&AAnswer questions from docs
Database AccessSQL queries, CRUDAI-powered data analysis
API IntegrationExternal service callsBooking, CRM updates
File SystemRead/write filesCode generation, reports
CommunicationEmail, Slack, etc.Automated notifications

Experience:

Case Studies: Agentic AI Knowledge Systems

Related Technologies: Anthropic Claude, AI Agents, LangChain, FastAPI, Python

$ ls -la projects/

Enterprise Knowledge Agent

@ Sparrow Intelligence
Challenge:

Build an AI agent that can access company knowledge bases, databases, and APIs.

Solution:

Custom MCP servers for database access, document retrieval, and API integration with proper access controls.

Result:

AI agents that can safely access enterprise data with audit trails.

Legal Research Assistant

@ Anaqua
Challenge:

Enable AI to search and retrieve legal documents with proper context.

Solution:

MCP server wrapping patent database with semantic search, citation retrieval, and structured output.

Result:

Lawyers can ask natural language questions and get accurate, cited answers.

$ diff me competitors/

+ Early adopter of MCP—building since protocol launch
+ Enterprise security focus—proper access controls
+ Full-stack capability—servers, clients, and infrastructure
+ Production experience—not just demos
+ LangChain/LangGraph integration expertise

Build Your MCP Integration

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