Concierge - Production Grade MCP Framework

The Plan backend gives the LLM one meta-tool: execute_plan(steps). The LLM submits a JSON plan:a list of steps that execute sequentially, with later steps able to reference earlier results.

Setup

from concierge import Concierge, Config, ProviderType

app = Concierge(
    "my-server",
    config=Config(provider_type=ProviderType.PLAN),
)

How It Works

One LLM turn executes multiple tools in sequence. The LLM plans upfront, Concierge executes deterministically.

What the LLM Sends

{
  "steps": [
    {
      "id": "backup",
      "tool": "create_backup",
      "args": {"database": "prod"}
    },
    {
      "id": "validate",
      "tool": "validate_backup",
      "args": {
        "backup_id": {
          "output_by_reference": {"backup": ["backup_id"]}
        }
      }
    }
  ]
}

The output_by_reference resolves to the output of a previous step. Here, {"backup": ["backup_id"]} means: take the backup step’s result, then get ["backup_id"] from it.

Data Passing with Sharable

Only parameters annotated with Sharable() can receive referenced data:

from typing import Annotated
from concierge.core.sharable import Sharable

@app.tool()
def validate_backup(backup_id: Annotated[str, Sharable()]) -> dict:
    """Validate a backup by ID."""
    return {"valid": True, "backup_id": backup_id}

Only backward references are allowed:a step can only reference steps that come before it. No cycles, no self-references.

What the LLM Sees

A single tool:

[
  {
    "name": "execute_plan",
    "description": "Execute a sequential plan of tool calls.",
    "inputSchema": {
      "properties": {
        "steps": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "id": {"type": "string"},
              "tool": {"type": "string"},
              "args": {"type": "object"}
            }
          }
        }
      }
    }
  }
]

When to Use

Use Plan when your workflow involves multiple steps with data dependencies:the output of one tool feeds into the next.

Good fit:

Multi-step workflows (backup → validate → migrate)
Data pipelines where steps depend on previous results
When you need deterministic, auditable execution

Bad fit:

Simple single-tool calls (Plain is simpler)
Conditional logic or loops (use Code)
Dynamic workflows where the next step depends on runtime decisions

Welcome

Learn

Get Started

SDK

Backends

Platform

Plan

Setup

How It Works

What the LLM Sends

Data Passing with Sharable

What the LLM Sees

When to Use

​Setup

​How It Works

​What the LLM Sends

​Data Passing with Sharable

​What the LLM Sees

​When to Use

Setup

How It Works

What the LLM Sends

Data Passing with Sharable

What the LLM Sees

When to Use