Introduction
Welcome to the MCPLambda Documentation.
What is MCPLambda?
The artificial intelligence landscape is shifting towards autonomous agents. The Model Context Protocol (MCP) is the open standard that connects these AI models to external tools, databases, and APIs.
However, building agents is easy; managing the infrastructure to power them is hard. MCP servers are often stateful, requiring persistent connections and complex orchestration.
MCPLambda is a first-of-its-kind Platform-as-a-Service (PaaS) engineered to bridge this gap. It provides a “Vercel-like” experience for AI infrastructure, allowing you to deploy, manage, and scale MCP servers in seconds.
Why MCPLambda?
- Infrastructure-as-Code to Production-in-Seconds: Deploy directly from package managers (
npx,uvx), Git repositories, or Docker images. - Registry discovery: Browse a curated Official + Community catalog (ToolHive-backed) and install into a project — without giving up bring-your-own-server.
- Stateful by Design: Seamlessly handle long-lived connections and persistent storage requirements without manual Kubernetes configuration.
- Tool analytics: Per-deployment call volume, error rates, p50/p95 tool latency, and a searchable invocation log.
- CLI + agent control plane:
mcplfor terminals; service account tokens and the MCPLambda MCP server for AI-driven ops. - Security First: Fine-grained tool management and secure secret injection.
- Developer Velocity: Abstract away the complexities of Kubernetes, networking, and storage so you can focus on building agents.
Core Concepts
To get the most out of MCPLambda, it’s helpful to understand a few key concepts:
- Deployments: A running instance of your MCP server.
- Projects: Logical groupings of deployments, secrets, and team members.
- Registry: Curated Official and Community MCP servers you can search and install; BYO package/Git/image remains first-class.
- Tool analytics: Per-deployment metrics for tool calls, errors, latency, and invocation history.
- Service account tokens / MCP control plane:
mcpl_sat_tokens and the hosted MCPLambda MCP server (api.mcplambda.io/mcp) so agents and automation can manage infrastructure. - Compute Units: A flexible resource model that scales with your needs (Small, Medium, and Large profiles).
- Transport: How your agent communicates with the server (Standard I/O, Streamable HTTP, or SSE).
Next Steps
Deploy your first MCP server via dashboard or CLI.
Learn about GitOps, Docker, and Package-based deployments.
Browse and install Official and Community MCP servers.
Call volume, error rates, latency, and invocation logs.
Manage your infrastructure directly from the terminal.
Manage your deployments directly from your AI client using the MCPLambda MCP server.
Instructions for AI agents to deploy MCP servers on behalf of users.
Join our Discord community for support and discussion.