> ## Documentation Index
> Fetch the complete documentation index at: https://docs.hasmcp.com/llms.txt
> Use this file to discover all available pages before exploring further.

# When should you use the HasMCP Server to create new MCP Servers?

> Understanding the strategic use cases for allowing your LLM to self-provision entirely new MCP servers via the HasMCP MCP Server directly using HasMCP API.

# When to Self-Provision Using the HasMCP Server

By [installing the official HasMCP Server](/kb/install-official-hasmcp-server), you give your LLM (like Claude Desktop or Cursor) the ability to dynamically orchestrate the HasMCP API itself. This means the AI can build, configure, and manage other integrations natively.

But when is this "meta-orchestration" actually useful?

### 1. Rapid Prototyping and Discovery

If you are a developer exploring an unfamiliar API, building an MCP server manually can be tedious. Instead, you can give your LLM the official HasMCP tools, pass it a link to the third-party API documentation (or an OpenAPI spec), and say:

*"Read the Stripe API docs and build me a HasMCP server that exposes the `create_invoice` and `list_customers` endpoints."*

The LLM will automatically extract the JSON schemas, `POST` them to the `/providers` and `/tools` endpoints, and instantiate the server for you in seconds.

### 2. Autonomous Multi-Agent Systems

If you are building complex, multi-agent frameworks, agents often need to delegate tasks.

Instead of pre-configuring hundreds of distinct MCP servers for every possible edge case, a central "Orchestrator Agent" equipped with the HasMCP tools can **spin up distinct, single-purpose MCP servers on demand** for its sub-agents.

* Example: An Orchestrator realizes it needs to audit a Jenkins server. It dynamically creates a "Jenkins Auditor MCP Server" in HasMCP, generates a scoped access token, and passes that token to a specialized sub-agent. Once the job finishes, the Orchestrator deletes the server.

### 3. Dynamic Tool Updates

APIs change constantly. When a vendor updates their endpoint (e.g., adding a new required `query` parameter), static MCP implementations break until a human developer rewrites the connector code.

An AI agent equipped with the HasMCP Server tools can detect the API failure, read the vendor's updated changelog, and execute an `UPDATE` request to the HasMCP `/tools` endpoint modifying the `inputSchema` natively—fixing its own broken tools without human intervention.

### 4. Client Onboarding Automation

If you manage a SaaS platform that provisions individual LLM agents for each of your customers, you can automate their setup.

You can write a script or prompt an administrative agent to:

1. Create a segregated Provider for the new customer.
2. Ingest their specific REST API schema.
3. Generate a dedicated MCP Server mapping to their internal REST API.
4. Output the final Server Token.

Using the HasMCP Server allows you to treat your AI agent layer as purely configurable infrastructure.