Model Context Protocol (MCP) support with new use case (#42)

* initial mcp

* food ordering with mcp

* prompt eng

* splitting out goals and updating docs

* a diff so I can get tests from codex

* a diff so I can get tests from codex

* oops, missing files

* tests, file formatting

* readme and setup updates

* setup.md link fixes

* readme change

* readme change

* readme change

* stripe food setup script

* single agent mode default

* prompt engineering for better multi agent performance

* performance should be greatly improved

* Update goals/finance.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* Update activities/tool_activities.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* co-pilot PR suggested this change, and now fixed it

* stronger wording around json format response

* formatting

* moved docs to dir

* moved image assets under docs

* cleanup env example, stripe guidance

* cleanup

---------

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

docs/architecture.md

@@ -0,0 +1,74 @@
+# Elements
+These are the main elements of this system. See [architecture decisions](./architecture-decisions.md) for information beind these choices.
+In this document we will explain each element and their interactions, and then connect them all at the end.
+<img src="./assets/Architecture_elements.png" width="50%" alt="Architecture Elements">
+
+## Workflow 
+This is a [Temporal Workflow](https://docs.temporal.io/workflows) - a durable straightforward description of the process to be executed. See [agent_goal_workflow.py](./workflows/agent_goal_workflow.py).
+Temporal is used to make the process scalable, durable, reliable, secure, and visible.
+
+### Workflow Responsibilities:
+- Orchestrates interactive loops:
+    - LLM Loop: Prompts LLM, durably executes LLM, stores responses
+    - Interactive Loop: Elicits responses from input (in our case a human) and validates input responses
+    - Tool Execution Loop: Durably executes Tools
+- Keeps record of all interactions ([Signals, Queries, Updates](https://docs.temporal.io/develop/python/message-passing))
+- Handles failures gracefully
+- Input, LLM and Tool interaction history stored for debugging and analysis
+
+## Activities
+These are [Temporal Activities](https://docs.temporal.io/activities). Defined as simple functions, they are auto-retried async/event driven behind the scenes. Activities durably execute Tools and the LLM. See [a sample activity](./activities/tool_activities.py).
+
+## Tools 
+Tools define the capabilities of the system. They are simple Python functions (could be in any language as Temporal supports multiple languages).
+They are executed by Temporal Activities. They are “just code” - can connect to any API or system. They also are where the deterministic business logic is: you can validate and retry actions using code you write.
+Failures are handled gracefully by Temporal.
+
+Activities + Tools turn the probabalistic input from the user and LLM into deterministic action.
+
+## Prompts
+Prompts are where the instructions to the LLM are. Prompts are made up of initial instructions, goal instructions, and tool instructions. 
+See [agent prompts](./prompts/agent_prompt_generators.py) and [goal & tool prompts](./tools/goal_registry.py). 
+
+This is where you can add probabalistic business logic to
+- to control process flow
+- describe what to do
+- give examples of interactions
+- give instruction and validation for the LLM
+
+## LLM
+Probabalistic execution: it will _probably_ do what you tell it to do.
+Turns the guidance from the prompts (see [agent prompts](./prompts/agent_prompt_generators.py) and [goal prompts](./tools/goal_registry.py)) into 
+You have a choice of providers - see [setup](./setup.md). 
+The LLM:
+- Drives toward the initial Goal and any subsequent Goals selected by user
+- Decides what to do based on input, such as:
+    - Validates user input for Tools
+    - Decides when to execute Tools
+    - Decides on next step for Goal
+- Formats input and interprets output for Tools
+- is executed by Temporal Activities
+    - API failures and logical failures are handled transparently
+
+## Interaction
+Interaction is managed with Temporal Signals and Queries. These are durably stored in Workflow History. 
+History can be used for analysis and debugging. It's all “just code” so it's easy to add new Signals and Queries. 
+Input can be very dynamic, just needs to be serializable.
+
+The Workflow executes the Interaction Loop: gathering input, validating input, and providing a response:
+
+![Interaction Loop](./assets/interaction_loop.png)
+
+Here's a more detailed example for gathering inputs for Tools:
+
+![Tool Gathering](./assets/argument_gathering_cycle.png)
+
+# Architecture Model
+Now that we have the pieces and what they do, here is a more complete diagram of how the pieces work together: 
+
+
+![Architecture](./assets/ai_agent_architecture_model.png "Architecture Model")
+
+
+# Adding features
+Want to add more Goals and Tools? See [adding goals and tools](./adding-goals-and-tools.md). Have fun!