Recipe → MCP Bridge

Turn any PraisonAI recipe into a scoped MCP server for Claude Desktop, Cursor, and Windsurf

Serve any recipe as its own MCP server, so an MCP client sees exactly one recipe’s tools, prompts, and resources — safe-mode on by default.

Quick Start

1. Build an agent recipe

   from praisonaiagents import Agent
   
   agent = Agent(
       name="support-reply",
       instructions="Draft a friendly reply to a customer support ticket.",
   )
   agent.start("Customer says their order is late.")

   Save the recipe as support-reply so the bridge can find it by name.

2. Serve the recipe as an MCP server

   praisonai mcp serve-recipe support-reply --transport stdio

   Only this recipe’s tools and prompts are exposed — nothing else.

3. Point your MCP client at it

   praisonai mcp config-generate-recipe support-reply --client claude-desktop

   Paste the printed block into your Claude Desktop config and the recipe appears as a tool.

How It Works

A recipe maps to MCP primitives: agent tools become MCP tools, agent instructions become MCP prompts, and the recipe config becomes MCP resources — all behind a deny-list gate.

CLI Commands

Five commands work with recipes over MCP — one to discover, four to serve. Each works as praisonai mcp <command> inside the full stack, or praisonai-mcp <command> standalone.

list-recipes

List every recipe the discovery layer can see — custom (~/.praison, ~/.config), project (./.praison), and bundled (agent_recipes). You need a recipe name before you can serve-recipe.

praisonai mcp list-recipes
praisonai-mcp list-recipes
praisonai mcp list-recipes --source package
praisonai mcp list-recipes --tags video,social
praisonai mcp list-recipes --json

Source labels — what each one lists:

Expected output (default, non-JSON):

Available Recipes (2):

  • data-analyst (v1.0.0)
    Data analysis template using pandas tools

  • support-reply (v1.0.0)
    Draft a friendly reply to a customer support ticket

JSON output (--json):

{
  "recipes": [
    {
      "name": "data-analyst",
      "version": "1.0.0",
      "description": "Data analysis template using pandas tools",
      "source": "package",
      "tags": ["data", "pandas"]
    }
  ]
}

Which recipe do I serve?

Pair list-recipes with serve-recipe — discover first, then serve.

serve-recipe

Turn a recipe into a scoped MCP server.

praisonai mcp serve-recipe support-reply --transport stdio
praisonai-mcp serve-recipe support-reply --transport http-stream --port 8080

validate-recipe

Check a recipe is safe to host before serving it.

praisonai mcp validate-recipe support-reply

Warnings flag recipes with no tools or with shell/exec tools.

inspect-recipe

Show the tools, resources, and prompts a recipe would expose.

praisonai mcp inspect-recipe support-reply --tools

With no flags, it shows everything.

config-generate-recipe

Emit a client config block that points at this recipe.

praisonai mcp config-generate-recipe support-reply --client claude-desktop

Which Serve Command

Three commands serve MCP — pick the one that matches your scope.

Allow / Deny Lists and Safe Mode

Safe mode is on by default and filters every tool through a deny-list before it reaches a client.

Default denied tools (from DEFAULT_DENIED_TOOLS in the adapter):

A tool is blocked when a denied name matches part of the tool name in either direction. Set an allow-list and only those tools pass; leave it empty and the deny-list applies.

from praisonai_mcp.mcp_server import RecipeMCPConfig

# Allow-list: only these tools pass
config = RecipeMCPConfig(
    recipe_name="support-reply",
    tool_allowlist=["search", "summarize"],
)

# Deny-list: extend the defaults
config = RecipeMCPConfig(
    recipe_name="support-reply",
    tool_denylist=["shell.exec", "file.delete", "my.custom.tool"],
)

Python API

Load a recipe and run it as an MCP server in three lines.

from praisonai_mcp.mcp_server import RecipeMCPAdapter

adapter = RecipeMCPAdapter("support-reply")
adapter.load()
adapter.to_mcp_server().run(transport="stdio")

RecipeMCPAdapter

RecipeMCPConfig

End-to-End Example

Go from an agent recipe to a working Claude Desktop tool in three steps.

1. Write the recipe

   from praisonaiagents import Agent
   
   agent = Agent(
       name="support-reply",
       instructions="Draft a friendly reply to a customer support ticket.",
   )

2. Serve it

   praisonai mcp serve-recipe support-reply --transport stdio

3. Generate the client config

   praisonai mcp config-generate-recipe support-reply --client claude-desktop

   Standalone (praisonai-mcp)

   {
     "mcpServers": {
       "support-reply": {
         "command": "praisonai-mcp",
         "args": ["serve-recipe", "support-reply", "--transport", "stdio"]
       }
     }
   }

   Umbrella (praisonai)

   {
     "mcpServers": {
       "support-reply": {
         "command": "praisonai",
         "args": ["mcp", "serve-recipe", "support-reply", "--transport", "stdio"]
       }
     }
   }

Paste that block into Claude Desktop’s config and the support-reply recipe appears as a tool.

Best Practices

Related