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# Concierge AI 🚀
<strong>The fabric for reliable MCP servers and AI applications.</strong>
[](https://docs.getconcierge.app)
[](https://discord.gg/bfT3VkhF)
[](https://pypi.org/project/concierge-sdk)
[](https://pypi.org/project/concierge-sdk)
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The [Model Context Protocol](https://modelcontextprotocol.io) (MCP) is a standardized way to connect AI agents to tools. Instead of exposing a flat list of every tool on every request, Concierge progressively discloses only what's relevant. Concierge guarantees deterministic results and reliable tool invocation.
## Getting Started
> [!NOTE]
> Concierge requires Python 3.9+. We recommend installing with [uv](https://docs.astral.sh/uv/) for faster dependency resolution, but pip works just as well.
```bash
pip install concierge-sdk
```
**Scaffold a new project:**
```bash
concierge init my-store # Generate a ready to run project
cd my-store # Enter project
python main.py # Start the MCP server
```
**Or wrap an existing MCP server** two lines, nothing else changes:
```python
# Before
from mcp.server.fastmcp import FastMCP
app = FastMCP("my-server")
# After: just wrap it
from concierge import Concierge
app = Concierge(FastMCP("my-server"))
```
> [!TIP]
> Concierge works at the MCP protocol level. It dynamically changes which tools are returned by `tools/list` based on the current workflow step. The agent and client don't need to know Concierge exists, they just see fewer, more relevant tools at each point.
<br />
```python
from concierge import Concierge
from mcp.server.fastmcp import FastMCP
app = Concierge(FastMCP("my-server"))
# Your @app.tool() decorators stay exactly the same.
# You can additionally add app.stages and app.transitions.
```
> [!NOTE]
> The wrap and go gives you progressive tool disclosure immediately. Add `app.stages` and `app.transitions` when you want full workflow control, no code changes required.
<br />
## Usage
### Group tools into steps
Instead of exposing everything at once, group related tools together. Only the current step's tools are visible to the agent:
```python
app.stages = {
"browse": ["search_products", "view_product"],
"cart": ["add_to_cart", "remove_from_cart", "view_cart"],
"checkout": ["apply_coupon", "complete_purchase"],
}
```
### Define transitions
Control which steps can follow which. The agent moves forward (or backward) only along paths you allow:
```python
app.transitions = {
"browse": ["cart"], # Can only move to cart
"cart": ["browse", "checkout"], # Can go back or proceed
"checkout": [], # Terminal step
}
```
<details>
<summary><b>Share state between steps</b></summary>
<br>
Pass data between workflow steps without round-tripping through the LLM. State is session-scoped and works across distributed replicas:
```python
# In the "browse" step - save a selection
app.set_state("selected_product", {"id": "p1", "name": "Laptop"})
# In the "cart" step retrieve it directly
product = app.get_state("selected_product")
```
</details>
<details>
<summary><b>Scale with semantic search</b></summary>
<br>
When you have hundreds of tools, enable semantic search to collapse your entire API behind two meta-tools:
```python
from concierge import Concierge, Config, ProviderType
app = Concierge("large-api", config=Config(
provider_type=ProviderType.SEARCH,
max_results=5,
))
```
No matter how many tools you register, the agent only ever sees:
```
search_tools(query: str) → Find tools by description
call_tool(tool_name: str, args: dict) → Execute a discovered tool
```
</details>
### Run over HTTP
Concierge supports multiple transports. Use streamable HTTP for web deployments:
```python
# Streamable HTTP (recommended for web)
http_app = app.streamable_http_app()
# Or run over stdio (default, for CLI-based clients)
app.run()
```
> [!TIP]
> All of the above: stages, transitions, state, semantic search are optional and independent. Use any combination. Start simple and add structure as your workflow grows.
## Features
| | |
|:--|:--|
| **Progressive Disclosure**: Only expose the tools that matter right now. Fewer tools in context means less confusion and lower cost. | **Enforced Tool Ordering**: Define which tools unlock which. The agent follows your business logic, not its own guesses. |
| **Shared State**: Pass data between workflow steps server-side. No tool-call chaining through the LLM, no re-injecting data into prompts. | **Semantic Search**: For large APIs (100+ tools), collapse everything behind two meta-tools. The agent searches by description, then invokes. |
| **Protocol Compatible**: Wraps any MCP server. Your existing `@app.tool()` decorators, resources, and prompts work unchanged. | **Session Isolation**: Each conversation gets its own workflow state. Atomic, consistent, works across distributed replicas. |
| **Multiple Transports**: Run over stdio, streamable HTTP, or SSE. Deploy anywhere: serverless, containers, bare metal. | **Scaffolding CLI**: `concierge init` generates a ready to run project with tools, stages, and transitions wired up ready to go. |
## Example Concierge Application
A complete e-commerce workflow in under 30 lines:
```python
from concierge import Concierge
app = Concierge("shopping")
@app.tool()
def search_products(query: str) -> dict:
"""Search the product catalog."""
return {"products": [{"id": "p1", "name": "Laptop", "price": 999}]}
@app.tool()
def add_to_cart(product_id: str) -> dict:
"""Add a product to the cart."""
cart = app.get_state("cart", [])
cart.append(product_id)
app.set_state("cart", cart)
return {"cart": cart}
@app.tool()
def checkout(payment_method: str) -> dict:
"""Complete the purchase."""
cart = app.get_state("cart", [])
return {"order_id": "ORD-123", "items": len(cart), "status": "confirmed"}
app.stages = {
"browse": ["search_products"],
"cart": ["add_to_cart"],
"checkout": ["checkout"],
}
app.transitions = {
"browse": ["cart"],
"cart": ["browse", "checkout"],
"checkout": [],
}
app.run() # Start over stdio
```
The agent starts at `browse`. It can move to `cart`, then to `checkout`. It cannot call `checkout` from `browse`. Concierge enforces this at the protocol level, no prompt engineering required.
<br />
## Documentation
Full guides, API reference, and deployment patterns are available at **[docs.getconcierge.app](https://docs.getconcierge.app)**.
<br />
## Community
- [Discord](https://discord.gg/bfT3VkhF): Ask questions, share what you're building, get help.
- [Issues](https://github.com/concierge-hq/concierge/issues): Report bugs or request features.
- [Discussions](https://github.com/concierge-hq/concierge/discussions): Longer form discussions and RFCs.
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We are building the agentic web. Come <a href="mailto:arnav@getconcierge.app">join us</a>.
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