MCP Servers & Connectors
Give your AI secure hands into every enterprise system
We build Model Context Protocol servers and enterprise connectors that let AI agents read from and act on your systems of record. Salesforce, SAP, Slack, Snowflake, SharePoint, and 100+ more, with authentication, audit trails, and permission scoping built in from the first line of code.
Overview
An LLM without tools is a consultant who can't touch the keyboard.
The Model Context Protocol (MCP) is the open standard that connects AI models to external systems, the USB-C of enterprise AI. But a production MCP server is more than a thin API wrapper: it needs OAuth flows that never expose credentials to the model, permission scoping so an agent can read invoices but not approve them, rate limiting, structured error handling the model can recover from, and audit logs your compliance team will sign off on. That's what we build, custom MCP servers for your proprietary systems and hardened connectors for your SaaS stack.
What's included
Custom MCP server development
Purpose-built MCP servers for your internal systems, legacy ERPs, proprietary databases, homegrown tools, exposing exactly the capabilities agents need, and nothing more.
Enterprise SaaS connectors
Production-hardened connectors for Salesforce, SAP, Workday, ServiceNow, Slack, Jira, Snowflake, SharePoint, and 100+ systems, with native API pagination, retry, and schema handling.
Credential isolation & OAuth
Tokens live in the server layer, never in the model context. Per-user OAuth delegation means the agent acts with the permissions of the human it serves, no shared service accounts.
Permission scoping & policy
Tool-level allow/deny policies, read/write separation, approval gates for irreversible actions, and tenant isolation, enforced in the server, not the prompt.
Audit & observability
Every tool call logged with caller, arguments, result, and latency, streamed to your SIEM. Compliance teams get the evidence trail; engineers get the debugging surface.
Tool design for model reliability
Schema design, error messages, and response shaping tuned so models call tools correctly, the difference between an agent that works in demos and one that works in production.
Developer experience
Simple API. Powerful results.
Integrate in minutes with our SDK. Full TypeScript support, comprehensive documentation, and live examples for every feature.
How it works
From setup to production
System & capability mapping
We inventory the systems your agents need, the actions they should perform, and the permission boundaries that must hold, producing a tool catalog before any code.
Security architecture
Credential isolation, OAuth delegation, scoping policy, and audit pipeline are designed with your security team and signed off before implementation.
Server & connector build
MCP servers built with typed schemas, structured errors, and model-friendly tool descriptions, tested against the actual models that will call them.
Agent integration testing
We run agents against the servers in a sandbox, measuring tool-call accuracy and failure recovery, tuning schemas and descriptions until call success rates are production-grade.
Deployment & governance
Production rollout with monitoring dashboards, per-tool usage analytics, SIEM integration, and a governance process for adding new tools safely.
FAQ
Common questions
Get started
Connect AI to the systems that run your business
Talk to an expert and get a tailored implementation plan within 48 hours.