Concierge - Production Grade MCP Framework

The Problem

Standard MCP servers expose every tool at once. For a server with 50 tools, the LLM receives all 50 tool definitions on every single turn, even if only 3 are relevant right now. This causes:

Wasted tokens: 80%+ of the context window is irrelevant tool schemas
Poor tool selection: the LLM frequently picks the wrong tool
No guardrails: nothing prevents calling checkout before add_to_cart
No state: the server has no awareness of previous tool executions or state changes between calls

What Concierge Does

Concierge is a production MCP framework that wraps your tools with structure. It’s a drop-in replacement for FastMCP that adds four capabilities:

Progressive Tool Disclosure

Instead of dumping all tools at once, Concierge shows only the tools relevant to the current stage:

Standard MCP:    LLM sees 50 tools → picks wrong one → retry → retry → success
Concierge:       LLM sees 3 tools  → picks right one → success

Staged Workflows

Define stages (groups of tools) and transitions (allowed paths between stages). The LLM can only move forward through your defined flow:

Concierge auto-generates a proceed_to_next_stage tool so the LLM can navigate between stages. The LLM can only transition to stages you’ve explicitly allowed.

Code-Based Execution

Instead of calling tools one-by-one (each consuming context), the agent writes a Python script that calls multiple tools in sequence. One LLM turn does the work of many:

Without Code Mode (5 LLM turns)
With Code Mode (1 LLM turn)

Turn 1: search_products("laptop") → results
Turn 2: get_details("p1") → details
Turn 3: add_to_cart("p1") → cart
Turn 4: apply_coupon("SAVE10") → discount
Turn 5: checkout("credit_card") → order

results = await tools.search_products(query="laptop")
details = await tools.get_details(product_id=results[0]["id"])
await tools.add_to_cart(product_id=details["id"])
await tools.apply_coupon(code="SAVE10")
order = await tools.checkout(payment_method="credit_card")
print(order)

Code mode runs in a sandboxed environment:no imports, no eval, no file access. Only your registered tools are available.

Persistent State

Per-session key-value storage that persists across stage transitions:

# In the "browse" stage
app.set_state("viewed_products", ["p1", "p2"])

# Later, in the "checkout" stage
viewed = app.get_state("viewed_products", [])

State can be backed by in-memory storage (default) or Postgres for distributed deployments.

How It Compares

Feature	Plain MCP	Concierge
Tool exposure	All at once	Progressive per-stage
Ordering	None	Enforced transitions
Session state	None	Built-in key-value store
Context usage	High	Minimal (code mode)
Reliability	LLM-dependent	Deterministic flows
Migration effort	N/A	One-line import swap

One-Line Migration

# Before:plain MCP
from mcp.server.fastmcp import FastMCP
app = FastMCP("my-server")

# After:Concierge (all existing @app.tool() decorators keep working)
from concierge import Concierge
app = Concierge("my-server")

Get Started

Install Concierge and build your first staged workflow →

Welcome

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SDK

Backends

Platform

What is Concierge?

The Problem

What Concierge Does

Progressive Tool Disclosure

Staged Workflows

Code-Based Execution

Persistent State

How It Compares

One-Line Migration

Get Started

​The Problem

​What Concierge Does

​Progressive Tool Disclosure

​Staged Workflows

​Code-Based Execution

​Persistent State

​How It Compares

​One-Line Migration

Get Started

The Problem

What Concierge Does

Progressive Tool Disclosure

Staged Workflows

Code-Based Execution

Persistent State

How It Compares

One-Line Migration