AI & Automation
MCP Tooling
If a platform has a documented API and you have a compute host, an LLM can use it. The bridge between the two is an MCP server.
The Gap
The gap between official and capable.
The Model Context Protocol is a standardized interface that translates an AI model's intentions into API calls. Most platforms that publish official MCP servers expose a carefully curated subset of their API — the most common use cases, designed for broad accessibility. For general workflows, that's often sufficient. For production automation, it usually isn't.
JumpCloud publishes an official MCP with roughly a dozen tools. The JumpCloud API has hundreds of endpoints — user lifecycle management, device policy assignment, RADIUS configuration, group membership, LDAP integration, conditional access controls, hardware inventory, and more. The official MCP exposes a fraction of that surface.
We built a custom JumpCloud MCP that exposes the full API. The result: Claude can manage an entire JumpCloud tenant through natural language — provisioning users, assigning device policies, configuring SSO integrations, auditing access, revoking credentials. What previously required a skilled administrator working through a UI now executes through an agent that understands your intent and acts on it precisely.
The same principle applies to any platform with a documented API. If it can be called programmatically, it can become a tool.
Model Compatibility
Works across models
MCP servers are model-agnostic by design. A server we build works with Claude, ChatGPT, Gemini, and any other MCP-compatible model. Our development and testing work most extensively with Claude — Anthropic's implementation of MCP is the most mature, and it's the model where we've stress-tested complex multi-tool agent workflows furthest. For deployments using other models, we validate compatibility explicitly.
How We Build It
From API documentation to production tool.
01
API analysis
We map the target platform’s API documentation, identifying the endpoints relevant to your use case, the authentication model, rate limits, and operational constraints.
02
Tool definition
Each endpoint becomes a named tool in the MCP server, with typed parameters, validation logic, descriptive documentation, and error handling. The tool descriptions are what the LLM reads to understand what it can do and how.
03
Deployment
The MCP server runs on your infrastructure or ours, available to any MCP-compatible model. Deployment can be local, containerized, or cloud-hosted depending on your security and latency requirements.
04
Testing and iteration
We verify that the model can reliably invoke each tool, interpret responses, handle errors gracefully, and chain multiple tools to complete multi-step tasks. This phase often surfaces gaps in API documentation that we work around before handoff.
What You Get
Any platform. Production-grade.
Any platform with a documented REST, GraphQL, or RPC API can become a tool your AI agents use reliably. We've built custom MCP servers for directory services, network monitoring platforms, ticketing systems, CRM APIs, financial platforms, and internal tooling.
Custom MCPs are infrastructure. We treat them that way — documented, maintained, and designed for production use rather than demonstration.
Let's Talk
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