MCP Server - Endprompt

Endprompt exposes its Admin API as an MCP (Model Context Protocol) server, allowing AI coding tools to manage your endpoints, prompts, and executions directly through natural conversation.

What is MCP?

MCP is an open protocol that lets AI assistants use external tools. When you connect Endprompt’s MCP server to your coding tool, the AI can:

Create and configure endpoints
Write and test Liquid prompt templates
Promote prompts to Live and set defaults
Execute endpoints and analyze results
Monitor stats and browse execution logs

All without you leaving your editor.

Supported Tools

The MCP server works with any client that supports the Streamable HTTP transport:

VS Code with GitHub Copilot or compatible extensions
Claude Desktop
Cursor
Windsurf
Any MCP-compatible client

Setup

Prerequisites

An Admin API key with the epa_ prefix
An MCP-compatible client

VS Code

Create a .vscode/mcp.json file in your project:

{
  "servers": {
    "endprompt": {
      "type": "http",
      "url": "https://api.endprompt.app/mcp",
      "headers": {
        "Authorization": "Bearer ${input:endpromptApiKey}"
      }
    }
  },
  "inputs": [
    {
      "id": "endpromptApiKey",
      "type": "promptString",
      "description": "Endprompt admin API key (epa_...)",
      "password": true
    }
  ]
}

VS Code will prompt you for the API key when the MCP server is first used. The key is stored securely per session.

Add .vscode/mcp.json to your .gitignore to avoid committing it, or use the ${input:...} pattern shown above to avoid hardcoding the key.

Claude Desktop

Add to your claude_desktop_config.json:

// ~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "endprompt": {
      "url": "https://api.endprompt.app/mcp",
      "headers": {
        "Authorization": "Bearer epa_your_admin_key_here"
      }
    }
  }
}

// %APPDATA%\Claude\claude_desktop_config.json
{
  "mcpServers": {
    "endprompt": {
      "url": "https://api.endprompt.app/mcp",
      "headers": {
        "Authorization": "Bearer epa_your_admin_key_here"
      }
    }
  }
}

Cursor

Add an MCP server in Cursor’s settings with:

URL: https://api.endprompt.app/mcp
Header: Authorization: Bearer epa_your_admin_key_here

Available Tools

Once connected, your AI assistant has access to 25 tools organized into three categories.

Endpoint Management

Tool	Description
`list_endpoints`	List all API endpoints for your tenant
`get_endpoint`	Get full endpoint details including field schemas
`create_endpoint`	Create a new API endpoint
`update_endpoint`	Update endpoint metadata (name, description, visibility)
`delete_endpoint`	Soft-delete an endpoint
`add_input_field`	Add a typed input field to an endpoint
`add_output_field`	Add a typed output field to an endpoint

Prompt Lifecycle

Tool	Description
`list_prompts`	List prompts for an endpoint with status filtering
`get_prompt`	Get full prompt details including Liquid template
`create_prompt`	Create a new prompt in Draft status
`update_prompt`	Update template or config (auto-versions on change)
`promote_prompt`	Promote from Draft to Live
`set_default_prompt`	Set a Live prompt as the endpoint default
`archive_prompt`	Archive a prompt
`unarchive_prompt`	Restore an archived prompt to Draft
`duplicate_prompt`	Clone a prompt as a new Draft
`list_prompt_versions`	View version history of a prompt
`validate_template`	Check Liquid syntax and variable references
`list_available_models`	List all available LLM models

Execution & Analytics

Tool	Description
`execute_endpoint`	Execute an endpoint with inputs, get LLM output
`get_endpoint_stats`	Execution stats (requests, latency, tokens, costs)
`get_prompt_stats`	Compare prompt performance within an endpoint
`list_execution_logs`	Browse execution history with filtering
`get_execution_log`	Full execution details (inputs, rendered prompt, response)

Example Conversations

”Create a sentiment analysis endpoint”

The AI assistant will:

Call list_available_models to show you model options
Call create_endpoint with your chosen name and path
Call add_input_field to define the text input
Call add_output_field for sentiment and confidence outputs
Call create_prompt with a Liquid template
Call validate_template to check for errors
Call promote_prompt to make it Live
Call set_default_prompt to activate it

”How are my endpoints performing?”

The AI assistant will:

Call get_tenant_stats for an overview
Call list_endpoints to show your endpoints
Call get_endpoint_stats for specific endpoints you ask about
Call get_prompt_stats to compare prompt performance

”Show me recent errors”

The AI assistant will:

Call list_execution_logs filtered to recent entries
Call get_execution_log for any failures to show full details
Analyze the rendered prompt and raw response to identify issues

Transport Details

Setting	Value
Protocol	MCP (Model Context Protocol)
Transport	Streamable HTTP
URL	`https://api.endprompt.app/mcp`
Auth	`Authorization: Bearer epa_...`
Session	Managed via `Mcp-Session-Id` header

The server also supports legacy SSE transport for older MCP clients.

​What is MCP?

​Supported Tools

​Setup

​Prerequisites

​VS Code

​Claude Desktop

​Cursor

​Available Tools

​Endpoint Management

​Prompt Lifecycle

​Execution & Analytics

​Example Conversations

​”Create a sentiment analysis endpoint”

​”How are my endpoints performing?”

​”Show me recent errors”

​Transport Details

What is MCP?

Supported Tools

Setup

Prerequisites

VS Code

Claude Desktop

Cursor

Available Tools

Endpoint Management

Prompt Lifecycle

Execution & Analytics

Example Conversations

”Create a sentiment analysis endpoint”

”How are my endpoints performing?”

”Show me recent errors”

Transport Details