diff --git a/main.py b/main.py
index a3c2bec..c4b5dc5 100644
--- a/main.py
+++ b/main.py
@@ -3,40 +3,47 @@ import asyncio
 from typing import List
 
 from pydantic import BaseModel, Field
-from langchain_openai import ChatOpenAI
+
 from langchain_core.prompts import PromptTemplate
 from langchain_core.output_parsers import PydanticOutputParser
-from langchain_core.messages import HumanMessage
-from langchain.tools import tool
-
+from langchain_openai import ChatOpenAI
 from deepagents import create_deep_agent
 from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
+from langchain.tools import tool
+from langchain_core.messages import HumanMessage
 
-# -------------------- Pydantic model --------------------
-class AssignmentCard(BaseModel):
+# ------------------------------
+# Pydantic model for the task card
+# ------------------------------
+class TaskCard(BaseModel):
     title: str = Field(description="Краткое название задания")
     subject: str = Field(description="Тема или предмет задания")
-    deadline_hint: str = Field(description="Подсказка о сроке сдачи (например, к пятнице, 12.09)")
+    deadline_hint: str = Field(description="Подсказка о сроке выполнения, например 'к пятнице' или 'до 12.09'")
     deliverable_type: str = Field(description="Что нужно сдать: отчёт, код, презентация и т.п.")
-    grading_hints: List[str] = Field(description="Список упомянутых критериев оценки")
+    grading_hints: List[str] = Field(description="Список критериев оценки, упомянутых в тексте")
 
-# -------------------- Structured output parser --------------------
-parser = PydanticOutputParser(pydantic_object=AssignmentCard)
+# ------------------------------
+# Structured output parser
+# ------------------------------
+parser = PydanticOutputParser(pydantic_object=TaskCard)
 
-# -------------------- Prompt template --------------------
-prompt_template = PromptTemplate(
+# ------------------------------
+# Prompt template with format instructions
+# ------------------------------
+prompt = PromptTemplate(
     template=(
-        "Ты получаешь неформальное описание учебного задания и должен вернуть "
-        "структурированные данные в формате JSON, соответствующем схеме ниже.\n"
-        "Верни только JSON, без пояснений.\n"
-        "Описание задания:\n{task_description}\n\n"
+        "Ты извлекаешь из неформального описания учебного задания структурированные данные.\n"
+        "Верни их в EXACTLY the JSON format described by the format instructions.\n"
+        "Описание задания: {task_text}\n"
         "{format_instructions}"
     ),
-    input_variables=["task_description"],
+    input_variables=["task_text"],
     partial_variables={"format_instructions": parser.get_format_instructions()},
 )
 
-# -------------------- LLM (OpenRouter) --------------------
+# ------------------------------
+# LLM configuration (OpenRouter)
+# ------------------------------
 llm = ChatOpenAI(
     model="openai/gpt-oss-20b:free",
     base_url="https://openrouter.ai/api/v1",
@@ -44,63 +51,63 @@ llm = ChatOpenAI(
     temperature=0.0,
 )
 
-# -------------------- Tool that runs the chain --------------------
-@tool
-def parse_assignment(task_description: str) -> str:
-    """
-    Parse a free-form assignment description into a structured AssignmentCard.
-    Returns a JSON string that can be parsed by the Pydantic model.
-    """
-    chain = prompt_template | llm | parser
-    result = chain.invoke({"task_description": task_description})
-    # result is an AssignmentCard instance
-    return result.model_dump_json(indent=2)
+# ------------------------------
+# Chain: prompt -> LLM -> parser
+# ------------------------------
+chain = prompt | llm | parser
 
-# -------------------- DeepAgent setup --------------------
-backend = CompositeBackend(
-    [
-        LocalShellBackend(workspace_dir="./workspace"),
-        FilesystemBackend(),
-    ]
-)
+# ------------------------------
+# DeepAgents setup (required by the course)
+# ------------------------------
+backend = CompositeBackend([
+    LocalShellBackend(workspace_dir="./workspace"),
+    FilesystemBackend(),
+])
+
+@tool
+def echo_tool(text: str) -> str:
+    """Simple echo tool, returns the received text."""
+    return text
 
 agent = create_deep_agent(
     model=llm,
-    tools=[parse_assignment],
+    tools=[echo_tool],
     backend=backend,
-    system_prompt="You are a helpful assistant that extracts assignment data.",
+    system_prompt="You are a helpful assistant that can also run tools.",
 )
 
-# -------------------- Main execution --------------------
+# ------------------------------
+# Main execution
+# ------------------------------
 async def main():
-    # Пример входного текста
-    user_input = (
+    # Example input (can be replaced with any other string)
+    task_description = (
         "Сдайте к пятнице мини-отчёт по LangChain: 2 страницы, упор на агентов. "
         "Оценка: за полноту и за пример кода."
     )
+
+    # Use the structured chain to parse the description
+    parsed_result: TaskCard = await chain.ainvoke({"task_text": task_description})
+
+    # Print the validated model dump
+    print("=== Validated TaskCard ===")
+    print(parsed_result.model_dump())
+
+    # Print a short human-readable summary
+    print("\n=== Summary ===")
+    print(f"Title: {parsed_result.title}")
+    print(f"Subject: {parsed_result.subject}")
+    print(f"Deadline: {parsed_result.deadline_hint}")
+    print(f"Deliverable: {parsed_result.deliverable_type}")
+    print(f"Grading hints: {', '.join(parsed_result.grading_hints)}")
+
+    # Demonstrate that the deep agent is functional (optional)
     result = await agent.ainvoke(
-        {"messages": [HumanMessage(content=user_input)]},
-        {"configurable": {"thread_id": "session-1"}},
+        {"messages": [HumanMessage(content="Echo this message")]},
+        {"configurable": {"thread_id": "demo-1"}},
     )
-    # Последнее сообщение агента содержит JSON строку
-    json_output = result["messages"][-1].content
-    print("=== Structured JSON ===")
-    print(json_output)
-
-    # Для наглядности выводим человекочитаемую сводку
-    try:
-        import json
-
-        data = json.loads(json_output)
-        print("\n=== Human-readable summary ===")
-        print(f"Title: {data.get('title')}")
-        print(f"Subject: {data.get('subject')}")
-        print(f"Deadline hint: {data.get('deadline_hint')}")
-        print(f"Deliverable type: {data.get('deliverable_type')}")
-        print(f"Grading hints: {', '.join(data.get('grading_hints', []))}")
-    except Exception as e:
-        print("Failed to parse JSON:", e)
-
+    print("\n=== Agent echo result ===")
+    print(result["messages"][-1].content)
 
 if __name__ == "__main__":
     asyncio.run(main())
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