MCP Server Development & Integration

Yes. Google officially supports MCP via the Gemini Python and JavaScript SDKs, the Gemini CLI, and Google Cloud-managed remote MCP servers for BigQuery, Google Maps, Compute Engine, and Kubernetes Engine. Terralogic builds MCP servers for Gemini 2.5 Pro and Gemini 2.5 Flash on Vertex AI using Google OAuth2 and service account authentication. The same MCP server layer can serve Claude, GPT-4o, and Gemini—the model is a configuration choice, not an architectural constraint.

Terralogic has delivered live MCP integrations—currently running in enterprise production—connecting AI agents to Workspace (Gmail, Drive, Calendar, BigQuery, and and Docs); Microsoft 365 (Outlook, Teams, SharePoint, and OneDrive); Slack; Cisco (Webex and networking infrastructure APIs); QuickBooks (accounting, invoicing, expenses, and payroll); Salesforce (CRM and leads); Jira and Confluence (tickets, sprints, and documentation); and custom internal enterprise REST and GraphQL APIs. Each integration includes OAuth2 or service account authentication, rate limiting, and full audit logging.

Yes. MCP is a model-agnostic protocol. Claude supports it natively. GPT-4o and Gemini connect via adapter layers that translate MCP tool definitions to their respective function-calling formats. Terralogic builds MCP servers that are compatible across all three platforms, so the same backend integration can serve different AI models depending on the use case or team preference.

Terralogic has delivered live MCP integrations to Google Workspace, Microsoft 365, Slack, Cisco, QuickBooks, Salesforce, and Jira that are running in production today—not reference implementations or demos. Terralogic also pairs AI engineering with UX design from Lollypop Design Studio, delivering the complete AI experience from server infrastructure through to the human interface. Most MCP developers provide backend plumbing alone. Terralogic ships the full system.

Terralogic’s MCP Discovery Sprint takes 1 week and delivers a complete architecture specification, model recommendation (Claude, GPT-4o, or Gemini), integration map, and build estimate. The build and deploy phase takes 4–10 weeks, depending on the number of systems being connected and the authentication complexity. The total timeline from first conversation to production is typically 5–12 weeks.

Anthropic offers native MCP support with first-class tool definitions, structured resource access, and prompt templates built directly into the Claude ecosystem. This results in highly predictable and reliable tool-calling, especially in complex, multi-step reasoning workflows and long-document processing. It is particularly strong where determinism, safety, and structured planning are critical. (Cloud-only deployment, supports OAuth2, API key, and JWT.)

OpenAI is ideal when you need robust multimodal capabilities (text, image, audio), strong developer tooling, and deep Microsoft ecosystem integration. MCP is implemented via an adapter layer, translating MCP tools into function-calling, which works reliably in production. It’s a strong choice for teams already invested in Azure, Microsoft 365, or existing OpenAI-based stacks and for use cases involving code generation, assistants, and cross-modal workflows. (Supports OAuth2 and API key, available via Azure OpenAI.)

Google provides official MCP support through Gemini SDKs (Python/JS) and CLI, along with managed MCP servers for services like BigQuery, Maps, GKE, and Compute Engine. This makes it especially powerful for data-intensive workflows, analytics agents, OCR/document processing, and Google Workspace-native use cases. Its tight integration with Vertex AI and Google Cloud services enables scalable, production-grade deployments with strong identity and access controls. (Supports OAuth2, service accounts, and IAP.)

Meta is the right choice when data privacy, infrastructure control, and cost efficiency at scale are key priorities. MCP is implemented via an adapter on self-hosted inference stacks such as vLLM, Ollama, or TGI, allowing full control over model deployment. This makes it ideal for regulated industries, air-gapped environments, or high-volume workloads where data cannot leave internal systems. (Supports internal authentication and mTLS, with full on-premise deployment.)

Terralogic delivers a fully production-ready MCP system, containerized and deployed in your infrastructure—compatible with Claude, GPT-4o, and Gemini. This is not a proof of concept but a system your team can operate and scale.

We don’t believe in “one-off” fixes. We use strictly typed, versioned definitions with rock-solid input/output schemas. This ensures that whether you’re running GPT-4o, Claude 3.5, or a local Llama model, the tools behave exactly the same way without you having to re-engineer the wheel every time you switch.

Security shouldn’t slow you down. We’ve built a flexible auth layer that manages the “messy” parts—OAuth2, JWTs, and service accounts—automatically. We handle token refreshes and per-user credential isolation out of the box, so your data stays locked down but accessible.

We bake compliance directly into the MCP layer. Every interaction is timestamped and logged—inputs, outputs, and user identities, giving you a bulletproof audit trail. Add in our built-in rate limiting, and you’ve got a system that’s as secure as it is scalable.

We don’t guess; we validate. Before deployment, we run a heavy-duty integration suite that stress-tests everything from tool accuracy to timeout handling and edge cases. We test it against your specific LLM environment to ensure that when you go live, the system actually holds up under pressure.

Terralogic provides complete, transparent documentation — including architecture, tool references, authentication setup, deployment runbooks, and maintenance guides — ensuring your team can fully own and extend the system.