MCP Servers ToolingGive AI agents tools, resources, and context
Model Context Protocol (MCP) lets you expose your data and actions to AI assistants like Claude — we build custom MCP servers that connect your database, APIs, and workflows to AI agents with proper auth and access control.
Model Context Protocol (MCP) is an open standard for connecting AI assistants to external data sources, tools, and workflows. MCP servers expose callable tools (actions) and readable resources (data) that AI clients like Claude, Cursor, and Windsurf can use to interact with your systems. We build custom MCP servers that connect your database, APIs, internal tools, and business workflows to AI agents — with proper authentication (OAuth 2.1), access control, and audit logging. MCP turns your existing infrastructure into AI-accessible capabilities.
Quick start
# Create an MCP server with TypeScript
npm init -y
npm install @modelcontextprotocol/sdk
# Or use the official create template
npx @modelcontextprotocol/create-server my-mcp-server
cd my-mcp-server
npm install
npm run buildRead the full documentation at modelcontextprotocol.io/
Tools & resources
Expose callable tools (actions) and readable resources (data) to any MCP-compatible AI client.
Auth & access control
OAuth 2.1 and API key auth for MCP servers — AI agents authenticate like any other client.
TypeScript SDK
Anthropic's official MCP SDK for TypeScript — typed tool definitions, request handlers, and transport layers.
Database access
Query your database via MCP tools — let AI assistants read schema, run safe queries, and summarise data.
Workflow automation
Trigger business logic from AI conversations — CRM updates, ticket creation, and notifications via MCP tools.
Remote & stdio transport
Serve MCP over HTTP/SSE for remote access or stdio for local integrations — flexible deployment options.
Why it's hard
Tool design for AI agents
AI agents interact differently than humans — tool descriptions, parameter naming, and response formatting must be optimized for LLM understanding, not just human readability.
Security and access control
MCP servers expose your systems to AI agents. Implementing proper authentication, authorization, and audit logging is critical to prevent unintended data access or actions.
Transport and deployment options
MCP supports stdio (local) and HTTP/SSE (remote) transports. Choosing the right transport depends on whether the server runs locally or as a remote service.
Best practices
Write clear tool descriptions
Tool names and descriptions are your API surface for AI agents — clear, specific descriptions with example inputs dramatically improve agent success rates.
Implement OAuth 2.1 for remote servers
Remote MCP servers should use OAuth 2.1 authentication — AI clients authenticate like any other API client with proper scopes and token management.
Return structured, concise responses
MCP tool responses are consumed by LLMs. Return structured data with clear labels — avoid dumping raw database rows or verbose error messages.
Useful resources
Frequently asked questions
Related technologies
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