Упрощение MCP workflow runner и обновить контракт /api/runs.

Перенесены planner/template хелперы в отдельные модули, выровнен формат статусов и сообщений в ответе, а также обновлены .env.example и README под текущие переменные и поведение API.

.env.example

@@ -1,9 +1,33 @@
+# Agent
 AGENT_ID=prisma-agent
+AGENT_MARKDOWN=false
+AGENT_DEBUG_MODE=true
+AGENT_INSTRUCTIONS="You are a helpful assistant. Answer briefly and clearly."
+
+# Agent model (Ollama)
 OLLAMA_MODEL_ID=gemma4:31b
 OLLAMA_HOST=http://localhost:11435
 OLLAMA_TEMPERATURE=0
-AGENT_MARKDOWN=false
-AGENT_DEBUG_MODE=true
-AGENT_INSTRUCTIONS=You are a helpful assistant. Answer briefly and clearly.
+
+# API runtime
 AGENT_OS_HOST=127.0.0.1
 AGENT_OS_PORT=7777
+
+# Planner
+PLANNER_ENABLED=false
+PLANNER_REPAIR_ATTEMPTS=3
+
+# Planner model (Polza)
+POLZA_BASE_URL=https://api.polza.ai/v1
+POLZA_MODEL_ID=google/gemma-4-31b-it
+POLZA_API_KEY=key
+POLZA_TEMPERATURE=0
+
+# MCP
+MCP_BASE_URL=http://127.0.0.1:8081/mcp
+MCP_TIMEOUT_SECONDS=10
+
+# Observability (Phoenix)
+PHOENIX_TRACING_ENABLED=false
+PHOENIX_COLLECTOR_ENDPOINT=http://localhost:6006
+PHOENIX_PROJECT_NAME=prisma-platform

.gitignore

@@ -25,3 +25,6 @@ dist/
 .vscode/
 .DS_Store
 .cursor
+
+# Cookbook code
+vendor/agno/cookbook/

README.md

@@ -1,8 +1,19 @@
 # Prisma Platform MVP
 
-Минимальный чат-агент на Agno + Ollama с рантаймом AgentOS.
+MVP-реализация сценарного раннера на Agno AgentOS.
 
-В этом проекте AgentOS работает как HTTP API сервер (FastAPI + Uvicorn).
+Текущая схема исполнения:
+
+- сценарий хранится в `scenarios/*.json`;
+- исполнение идет через `src/mcp_workflow_runner.py`;
+- каждый шаг вызывает MCP инструмент через `src/mcp_client.py`;
+- для подготовки аргументов шага используется planner-агент с моделью через `polza.ai`.
+
+## Требования
+
+- Python 3.10+
+- MCP endpoint (по умолчанию `http://127.0.0.1:8081/mcp`)
+- доступ к модели через `polza.ai` (`POLZA_API_KEY`)
 
 ## Текущая структура
 
@@ -11,11 +22,23 @@ prisma_platform/
 ├── .env
 ├── .env.example
 ├── requirements.txt
+├── scenarios/
+│   ├── index.json
+│   └── news_source_discovery/
+│       ├── v1.json
+│       └── v1_planner_repair.json
 └── src/
     ├── __init__.py
+    ├── api_routes.py
     ├── agent_os.py
     ├── agent_runner.py
-    └── main.py
+    ├── mcp_client.py
+    ├── mcp_workflow_runner.py
+    ├── observability.py
+    ├── scenario_store.py
+    ├── step_planner.py
+    ├── template.py
+    └── schemas.py
 ```
 
 ## Установка
@@ -29,44 +52,121 @@ cp .env.example .env
 
 ## Запуск
 
-Интерактивный режим чата:
+1) Поднимите MCP stub (из соседнего репозитория):
 
 ```bash
-python -m src.main
+cd /home/worker/projects/docker-service/mcp-stub
+docker compose up --build -d
 ```
 
-Режим одного сообщения:
+2) Запустите сервер AgentOS:
 
 ```bash
-python -m src.main --message "Привет, что ты умеешь?"
+cd /home/worker/projects/prisma_platform
+.venv/bin/python -m src.agent_os
 ```
 
-## Запуск AgentOS
+По умолчанию приложение доступно на `http://127.0.0.1:7777`.
 
-Запуск сервера AgentOS:
-
-```bash
-python -m src.agent_os
-```
-
-По умолчанию AgentOS работает на `http://127.0.0.1:7777`.
-
-Документация API доступна по адресам:
+Документация API:
 
 - `http://127.0.0.1:7777/docs`
 - `http://127.0.0.1:7777/redoc`
 
+## Запуск сценария через HTTP
+
+- `POST http://127.0.0.1:7777/api/runs`
+
+Тело запроса:
+
+```json
+{
+  "scenario_id": "news_source_discovery_v1",
+  "input": {
+    "url": "https://example.com/news"
+  }
+}
+```
+
+Пример:
+
+```bash
+curl -s -X POST "http://127.0.0.1:7777/api/runs" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "scenario_id": "news_source_discovery_v1",
+    "input": {
+      "url": "https://example.com/news"
+    }
+  }'
+```
+
+Успешный ответ содержит:
+
+- `status=success`
+- `message=""`
+- список `steps` со статусами и временем шагов
+- `output_summary`
+- `result` итогового шага
+
+При ошибке:
+
+- `status=failed`
+- `message` содержит текст ошибки
+
 ## Переменные окружения
 
-Основные переменные:
+Agent:
 
-- `AGENT_ID` (по умолчанию: `prisma-agent`)
-- `OLLAMA_MODEL_ID` (по умолчанию: `gemma4:31b`)
-- `OLLAMA_HOST` (по умолчанию: `http://localhost:11435`)
-- `OLLAMA_TEMPERATURE` (по умолчанию: `0`)
-- `AGENT_MARKDOWN` (по умолчанию: `false`)
-- `AGENT_DEBUG_MODE` (по умолчанию: `true`)
-- `AGENT_INSTRUCTIONS` (по умолчанию: `You are a helpful assistant. Answer briefly and clearly.`)
-- `AGENT_OS_HOST` (по умолчанию: `127.0.0.1`)
-- `AGENT_OS_PORT` (по умолчанию: `7777`)
+- `AGENT_ID` (default: `prisma-agent`)
+- `AGENT_MARKDOWN` (default: `false`)
+- `AGENT_DEBUG_MODE` (default: `true`)
+- `AGENT_INSTRUCTIONS`
+- `OLLAMA_MODEL_ID` (default: `gemma4:31b`)
+- `OLLAMA_HOST` (default: `http://localhost:11435`)
+- `OLLAMA_TEMPERATURE` (default: `0`)
+
+API runtime:
+
+- `AGENT_OS_HOST` (default: `127.0.0.1`)
+- `AGENT_OS_PORT` (default: `7777`)
+
+Planner:
+
+- `PLANNER_ENABLED` (default: `false`)
+- `PLANNER_REPAIR_ATTEMPTS` (default: `3`)
+
+Planner model (`polza.ai`):
+
+- `POLZA_BASE_URL` (default: `https://api.polza.ai/v1`)
+- `POLZA_MODEL_ID` (default: `google/gemma-4-31b-it`)
+- `POLZA_API_KEY` (required)
+- `POLZA_TEMPERATURE` (default: `0`)
+
+MCP:
+
+- `MCP_BASE_URL` (default: `http://127.0.0.1:8081/mcp`)
+- `MCP_TIMEOUT_SECONDS` (default: `10`)
+
+Phoenix tracing:
+
+- `PHOENIX_TRACING_ENABLED` (default: `false`)
+- `PHOENIX_COLLECTOR_ENDPOINT` (default: `http://localhost:6006`)
+- `PHOENIX_PROJECT_NAME` (default: `prisma-platform`)
+
+## Phoenix трассировка (локально)
+
+1) Включите трассировку в `.env`:
+
+```dotenv
+PHOENIX_TRACING_ENABLED=true
+PHOENIX_COLLECTOR_ENDPOINT=http://localhost:6006
+PHOENIX_PROJECT_NAME=prisma-platform
+```
+
+2) Запустите приложение:
+
+```bash
+.venv/bin/python -m src.agent_os
+```
 

requirements.txt

@@ -5,3 +5,5 @@ python-dotenv
 ollama
 socksio
 openai
+arize-phoenix-otel
+openinference-instrumentation-agno

scenarios/index.json

@@ -0,0 +1,6 @@
+{
+  "scenarios": {
+    "news_source_discovery_v1": "news_source_discovery/v1.json",
+    "news_source_discovery_v1_planner_repair": "news_source_discovery/v1_planner_repair.json"
+  }
+}

scenarios/news_source_discovery/v1.json

@@ -0,0 +1,105 @@
+{
+  "schema_version": "1",
+  "scenario_id": "news_source_discovery_v1",
+  "name": "News Source Discovery V1",
+  "description": "Find earliest news source using sequential MCP tools.",
+  "input_schema": {
+    "type": "object",
+    "required": [
+      "url"
+    ],
+    "properties": {
+      "url": {
+        "type": "string",
+        "description": "URL of source news article"
+      }
+    }
+  },
+  "steps": [
+    {
+      "name": "search_news_sources",
+      "type": "tool",
+      "tool": "search_news_sources",
+      "input": {
+        "url": {
+          "from": "input.url"
+        }
+      },
+      "required_input_fields": [
+        "url"
+      ]
+    },
+    {
+      "name": "parse_articles_batch",
+      "type": "tool",
+      "tool": "parse_article",
+      "foreach": {
+        "from": "steps.search_news_sources.payload.items",
+        "as": "item"
+      },
+      "input": {
+        "url": {
+          "from": "item.url"
+        }
+      },
+      "collect": {
+        "url": {
+          "from": "tool.payload.url"
+        },
+        "title": {
+          "from": "tool.payload.title"
+        },
+        "text": {
+          "from": "tool.payload.text"
+        }
+      },
+      "collect_key": "items"
+    },
+    {
+      "name": "extract_publication_date_batch",
+      "type": "tool",
+      "tool": "extract_publication_date",
+      "foreach": {
+        "from": "steps.parse_articles_batch.payload.items",
+        "as": "item"
+      },
+      "input": {
+        "article_text": {
+          "from": "item.text"
+        }
+      },
+      "collect": {
+        "url": {
+          "from": "item.url"
+        },
+        "title": {
+          "from": "item.title"
+        },
+        "published_at": {
+          "from": "tool.payload.published_at"
+        }
+      },
+      "collect_key": "items"
+    },
+    {
+      "name": "rank_sources_by_date",
+      "type": "tool",
+      "tool": "rank_sources_by_date",
+      "input": {
+        "items": {
+          "from": "steps.extract_publication_date_batch.payload.items"
+        }
+      }
+    },
+    {
+      "name": "generate_summary",
+      "type": "tool",
+      "tool": "generate_summary",
+      "input": {
+        "items": {
+          "from": "steps.rank_sources_by_date.payload.ranked_items"
+        }
+      }
+    }
+  ]
+}

scenarios/news_source_discovery/v1_planner_repair.json

@@ -0,0 +1,117 @@
+{
+  "schema_version": "1",
+  "scenario_id": "news_source_discovery_v1_planner_repair",
+  "name": "News Source Discovery V1 Planner Repair",
+  "description": "Test scenario with intentionally wrong input paths repaired by planner.",
+  "input_schema": {
+    "type": "object",
+    "required": [
+      "url"
+    ],
+    "properties": {
+      "url": {
+        "type": "string",
+        "description": "URL of source news article"
+      }
+    }
+  },
+  "steps": [
+    {
+      "name": "search_news_sources",
+      "type": "tool",
+      "tool": "search_news_sources",
+      "input": {
+        "url": {
+          "from": "input.url"
+        }
+      },
+      "required_input_fields": [
+        "url"
+      ]
+    },
+    {
+      "name": "parse_articles_batch",
+      "type": "tool",
+      "tool": "parse_article",
+      "foreach": {
+        "from": "steps.search_news_sources.payload.items",
+        "as": "item"
+      },
+      "input": {
+        "url": {
+          "from": "item.link"
+        }
+      },
+      "required_input_fields": [
+        "url"
+      ],
+      "collect": {
+        "url": {
+          "from": "tool.payload.url"
+        },
+        "title": {
+          "from": "tool.payload.title"
+        },
+        "text": {
+          "from": "tool.payload.text"
+        }
+      },
+      "collect_key": "items"
+    },
+    {
+      "name": "extract_publication_date_batch",
+      "type": "tool",
+      "tool": "extract_publication_date",
+      "foreach": {
+        "from": "steps.parse_articles_batch.payload.items",
+        "as": "item"
+      },
+      "input": {
+        "article_text": {
+          "from": "item.body"
+        }
+      },
+      "required_input_fields": [
+        "article_text"
+      ],
+      "collect": {
+        "url": {
+          "from": "item.url"
+        },
+        "title": {
+          "from": "item.title"
+        },
+        "published_at": {
+          "from": "tool.payload.published_at"
+        }
+      },
+      "collect_key": "items"
+    },
+    {
+      "name": "rank_sources_by_date",
+      "type": "tool",
+      "tool": "rank_sources_by_date",
+      "input": {
+        "items": {
+          "from": "steps.extract_publication_date_batch.payload.items"
+        }
+      },
+      "required_input_fields": [
+        "items"
+      ]
+    },
+    {
+      "name": "generate_summary",
+      "type": "tool",
+      "tool": "generate_summary",
+      "input": {
+        "items": {
+          "from": "steps.rank_sources_by_date.payload.items_ranked_typo"
+        }
+      },
+      "required_input_fields": [
+        "items"
+      ]
+    }
+  ]
+}

src/agent_os.py

@@ -1,15 +1,28 @@
 import os
 
 from dotenv import load_dotenv
+from fastapi import FastAPI
 
 from agno.os import AgentOS
 
+from src.api_routes import router as api_router
 from src.agent_runner import get_agent
+from src.observability import init_phoenix_tracing
 
 load_dotenv()
+_tracing_enabled = init_phoenix_tracing()
 
 _agent = get_agent()
-_agent_os = AgentOS(agents=[_agent])
+_base_app = FastAPI(
+    title="Prisma Platform API",
+    version="0.1.0",
+)
+_base_app.include_router(api_router)
+_agent_os = AgentOS(
+    agents=[_agent],
+    tracing=_tracing_enabled,
+    base_app=_base_app,
+)
 app = _agent_os.get_app()
 
 

src/agent_runner.py

@@ -47,9 +47,3 @@ def get_agent() -> Agent:
         debug_mode=debug_mode,
     )
     return _agent
-
-
-async def run_agent(message: str) -> str:
-    agent = get_agent()
-    response = await agent.arun(message)
-    return str(response.content)

src/api_routes.py

@@ -0,0 +1,14 @@
+from fastapi import APIRouter
+
+from src.mcp_workflow_runner import run_scenario
+from src.schemas import ScenarioRunRequest, ScenarioRunResponse
+
+router = APIRouter(prefix="/api", tags=["workflow"])
+
+
+@router.post("/runs", response_model=ScenarioRunResponse)
+async def post_run(request: ScenarioRunRequest) -> ScenarioRunResponse:
+    return await run_scenario(
+        scenario_id=request.scenario_id,
+        input_data=request.input,
+    )

src/main.py

@@ -1,43 +0,0 @@
-import argparse
-import asyncio
-
-from dotenv import load_dotenv
-
-from src.agent_runner import run_agent
-
-
-def build_parser() -> argparse.ArgumentParser:
-    parser = argparse.ArgumentParser(
-        description="Run base chat agent.",
-    )
-    parser.add_argument(
-        "--message",
-        help="Single message mode. If omitted, starts interactive chat.",
-    )
-    return parser
-
-
-async def _main() -> None:
-    load_dotenv()
-    args = build_parser().parse_args()
-
-    if args.message:
-        result = await run_agent(args.message)
-        print(result)
-        return
-
-    print("Chat mode started. Type 'exit' or 'quit' to stop.")
-    while True:
-        user_message = input("you> ").strip()
-        if not user_message:
-            continue
-        if user_message.lower() in {"exit", "quit"}:
-            print("Bye.")
-            break
-
-        result = await run_agent(user_message)
-        print(f"agent> {result}")
-
-
-if __name__ == "__main__":
-    asyncio.run(_main())

src/mcp_client.py

@@ -0,0 +1,56 @@
+from __future__ import annotations
+
+from datetime import timedelta
+import json
+import os
+from typing import Any
+
+from mcp import ClientSession
+from mcp.client.streamable_http import streamablehttp_client
+from mcp.types import TextContent
+
+
+def _mcp_url() -> str:
+    return os.getenv("MCP_BASE_URL", "http://127.0.0.1:8081/mcp")
+
+
+def _timeout_seconds() -> float:
+    value = os.getenv("MCP_TIMEOUT_SECONDS")
+    if value is None:
+        return 10.0
+    return float(value)
+
+
+async def call_mcp_tool(tool_name: str, arguments: dict[str, Any]) -> dict[str, Any]:
+    try:
+        async with streamablehttp_client(url=_mcp_url()) as session_params:
+            read, write = session_params[0:2]
+            async with ClientSession(
+                read,
+                write,
+                read_timeout_seconds=timedelta(seconds=_timeout_seconds()),
+            ) as session:
+                await session.initialize()
+                result = await session.call_tool(tool_name, arguments)
+    except TimeoutError as exc:
+        raise RuntimeError(f"MCP timeout: {tool_name}") from exc
+    except Exception as exc:
+        raise RuntimeError(f"MCP transport error: {tool_name}") from exc
+
+    if result.isError:
+        raise RuntimeError(f"MCP tool error: {tool_name}")
+
+    if isinstance(result.structuredContent, dict):
+        return result.structuredContent
+
+    for content_item in result.content:
+        if not isinstance(content_item, TextContent):
+            continue
+        try:
+            parsed = json.loads(content_item.text)
+        except json.JSONDecodeError:
+            continue
+        if isinstance(parsed, dict):
+            return parsed
+
+    raise RuntimeError(f"MCP tool returned invalid payload: {tool_name}")

src/mcp_workflow_runner.py

@@ -0,0 +1,385 @@
+from __future__ import annotations
+
+from copy import deepcopy
+from datetime import datetime, timezone
+import json
+import os
+from typing import Any, Awaitable, Callable
+
+from agno.workflow.step import Step, StepInput, StepOutput
+from agno.workflow.workflow import Workflow
+
+from src.mcp_client import call_mcp_tool
+from src.schemas import ScenarioRunResponse, StepState
+from src.scenario_store import ScenarioStoreError, load_scenario_definition
+from src.step_planner import plan_arguments, planner_enabled
+from src.template import (
+    missing_required_fields,
+    resolve_path,
+    resolve_template,
+    validate_required_fields,
+)
+
+
+def _env_int(name: str, default: int) -> int:
+    value = os.getenv(name)
+    return int(value) if value is not None else default
+
+
+def _utc_now_iso() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def _build_scope(session_state: dict[str, Any]) -> dict[str, Any]:
+    return {
+        "input": session_state.get("input", {}),
+        "steps": session_state.get("steps", {}),
+    }
+
+
+async def _prepare_arguments(
+    *,
+    step_name: str,
+    tool_name: str,
+    base_arguments: dict[str, Any],
+    required_fields: list[str],
+    scope: dict[str, Any],
+) -> dict[str, Any]:
+    final_arguments = deepcopy(base_arguments)
+    missing = missing_required_fields(final_arguments, required_fields)
+    if missing and planner_enabled():
+        max_attempts = _env_int("PLANNER_REPAIR_ATTEMPTS", 3)
+        for attempt in range(1, max_attempts + 1):
+            final_arguments = await plan_arguments(
+                step_name=step_name,
+                tool_name=tool_name,
+                base_arguments=final_arguments,
+                required_fields=required_fields,
+                scope=scope,
+                missing_fields=missing,
+                attempt_no=attempt,
+            )
+            missing = missing_required_fields(final_arguments, required_fields)
+            if not missing:
+                break
+    validate_required_fields(final_arguments, required_fields, step_name)
+    return final_arguments
+
+
+async def _execute_one_call(
+    *,
+    step_name: str,
+    tool_name: str,
+    required_fields: list[str],
+    input_template: Any,
+    scope: dict[str, Any],
+) -> tuple[dict[str, Any], dict[str, Any]]:
+    resolved = resolve_template(input_template, scope)
+    base_arguments = resolved if isinstance(resolved, dict) else {}
+    arguments = await _prepare_arguments(
+        step_name=step_name,
+        tool_name=tool_name,
+        base_arguments=base_arguments,
+        required_fields=required_fields,
+        scope=scope,
+    )
+    tool_response = await call_mcp_tool(tool_name, arguments)
+    return arguments, tool_response
+
+
+def _build_tool_executor(
+    step_spec: dict[str, Any],
+) -> Callable[[StepInput, dict[str, Any]], Awaitable[StepOutput]]:
+    step_name = str(step_spec["name"])
+    tool_name = str(step_spec["tool"])
+    input_template = step_spec.get("input", {})
+    foreach_spec = step_spec.get("foreach")
+    collect_template = step_spec.get("collect")
+    collect_key = str(step_spec.get("collect_key", "items")).strip() or "items"
+    required_fields = [
+        f for f in step_spec.get("required_input_fields", []) if isinstance(f, str)
+    ]
+
+    if isinstance(foreach_spec, dict):
+        foreach_from = str(foreach_spec.get("from", "")).strip()
+        item_alias = str(foreach_spec.get("as", "item")).strip() or "item"
+    else:
+        foreach_from = str(foreach_spec).strip() if isinstance(foreach_spec, str) else ""
+        item_alias = "item"
+
+    async def executor(_step_input: StepInput, session_state: dict[str, Any]) -> StepOutput:
+        started_at = _utc_now_iso()
+        scope = _build_scope(session_state)
+
+        try:
+            if foreach_from:
+                iterable = resolve_path(scope, foreach_from)
+                if not isinstance(iterable, list):
+                    raise ValueError(f"{step_name}: foreach source is not list")
+
+                collected: list[Any] = []
+                iteration_requests: list[dict[str, Any]] = []
+                iteration_responses: list[dict[str, Any]] = []
+                last_received_at: str | None = None
+                for index, item in enumerate(iterable):
+                    iteration_scope = {**scope, item_alias: item, "item": item, "index": index}
+                    arguments, tool_response = await _execute_one_call(
+                        step_name=step_name,
+                        tool_name=tool_name,
+                        required_fields=required_fields,
+                        input_template=input_template,
+                        scope=iteration_scope,
+                    )
+                    iteration_requests.append(arguments)
+                    iteration_responses.append(tool_response)
+                    received_at = tool_response.get("received_at")
+                    if isinstance(received_at, str) and received_at:
+                        last_received_at = received_at
+                    if collect_template is None:
+                        collected.append(tool_response.get("payload", {}))
+                    else:
+                        collected.append(
+                            resolve_template(
+                                collect_template,
+                                {**iteration_scope, "tool": tool_response},
+                            )
+                        )
+
+                step_payload = {
+                    "ok": True,
+                    "tool_name": tool_name,
+                    "payload": {collect_key: collected},
+                    "request": {
+                        "foreach_from": foreach_from,
+                        "count": len(iterable),
+                        "items": iteration_requests,
+                    },
+                    "response": {"items": iteration_responses},
+                    "received_at": last_received_at,
+                    "started_at": started_at,
+                    "finished_at": _utc_now_iso(),
+                }
+            else:
+                arguments, tool_response = await _execute_one_call(
+                    step_name=step_name,
+                    tool_name=tool_name,
+                    required_fields=required_fields,
+                    input_template=input_template,
+                    scope=scope,
+                )
+                step_payload = {
+                    "ok": bool(tool_response.get("ok", True)),
+                    "tool_name": tool_name,
+                    "payload": tool_response.get("payload", {}),
+                    "request": arguments,
+                    "response": tool_response,
+                    "received_at": tool_response.get("received_at"),
+                    "started_at": started_at,
+                    "finished_at": _utc_now_iso(),
+                }
+
+            session_state.setdefault("steps", {})[step_name] = step_payload
+            return StepOutput(
+                content=json.dumps(step_payload, ensure_ascii=False),
+                success=True,
+            )
+        except Exception as exc:
+            finished_at = _utc_now_iso()
+            error_payload = {
+                "ok": False,
+                "tool_name": tool_name,
+                "error": str(exc),
+                "started_at": started_at,
+                "finished_at": finished_at,
+            }
+            session_state.setdefault("steps", {})[step_name] = error_payload
+            raise RuntimeError(f"{step_name} failed: {exc}") from exc
+
+    return executor
+
+
+def _build_workflow(scenario_id: str, scenario: dict[str, Any]) -> Workflow:
+    raw_steps = scenario.get("steps")
+    if not isinstance(raw_steps, list) or not raw_steps:
+        raise ScenarioStoreError("Scenario must contain non-empty steps list")
+
+    workflow_steps: list[Step] = []
+    for raw_step in raw_steps:
+        if not isinstance(raw_step, dict):
+            raise ScenarioStoreError("Each scenario step must be object")
+        if raw_step.get("type") != "tool":
+            raise ScenarioStoreError("This runner supports only tool steps")
+
+        step_name = str(raw_step.get("name", "")).strip()
+        tool_name = str(raw_step.get("tool", step_name)).strip()
+        if not step_name or not tool_name:
+            raise ScenarioStoreError("Each tool step must contain non-empty name and tool")
+
+        workflow_steps.append(
+            Step(
+                name=step_name,
+                description=str(raw_step.get("description", step_name)),
+                executor=_build_tool_executor(raw_step),
+                max_retries=0,
+                on_error="fail",
+            )
+        )
+
+    return Workflow(
+        name=scenario_id,
+        description=str(scenario.get("description", "")),
+        steps=workflow_steps,
+    )
+
+
+_workflow_cache: dict[str, Workflow] = {}
+
+
+def _get_workflow(scenario_id: str, scenario: dict[str, Any]) -> Workflow:
+    cached = _workflow_cache.get(scenario_id)
+    if cached is not None:
+        return cached
+    workflow = _build_workflow(scenario_id, scenario)
+    _workflow_cache[scenario_id] = workflow
+    return workflow
+
+
+def _extract_output_summary(result: dict[str, Any] | None) -> str | None:
+    if not isinstance(result, dict):
+        return None
+    summary = result.get("summary")
+    if isinstance(summary, str) and summary:
+        return summary
+    payload = result.get("payload")
+    if isinstance(payload, dict):
+        payload_summary = payload.get("summary")
+        if isinstance(payload_summary, str) and payload_summary:
+            return payload_summary
+    return None
+
+
+def _build_step_states(
+    scenario: dict[str, Any],
+    steps_payloads: dict[str, Any],
+) -> list[StepState]:
+    raw_steps = scenario.get("steps")
+    if not isinstance(raw_steps, list):
+        return []
+
+    states: list[StepState] = []
+    for raw_step in raw_steps:
+        if not isinstance(raw_step, dict):
+            continue
+        name = str(raw_step.get("name", "")).strip()
+        if not name:
+            continue
+        payload = steps_payloads.get(name)
+        if not isinstance(payload, dict):
+            states.append(
+                StepState(
+                    node_id=name,
+                    status="queued",
+                    message="",
+                )
+            )
+            continue
+        ok = bool(payload.get("ok", False))
+        states.append(
+            StepState(
+                node_id=name,
+                status="success" if ok else "failed",
+                started_at=str(payload.get("started_at") or "") or None,
+                finished_at=str(payload.get("finished_at") or "") or None,
+                message="" if ok else str(payload.get("error", f"{name} failed")),
+            )
+        )
+    return states
+
+
+async def run_scenario(
+    *,
+    scenario_id: str,
+    input_data: dict[str, Any],
+) -> ScenarioRunResponse:
+    try:
+        scenario = load_scenario_definition(scenario_id)
+    except ScenarioStoreError as exc:
+        return ScenarioRunResponse(
+            scenario_id=scenario_id,
+            status="failed",
+            message=str(exc),
+            input=input_data,
+        )
+
+    scenario_name = str(scenario.get("name", scenario_id))
+    try:
+        workflow = _get_workflow(scenario_id, scenario)
+    except ScenarioStoreError as exc:
+        return ScenarioRunResponse(
+            scenario_id=scenario_id,
+            status="failed",
+            message=str(exc),
+            input=input_data,
+            scenario_name=scenario_name,
+        )
+
+    # Fresh per-run state that Agno owns during arun(..., session_state=...).
+    session_state: dict[str, Any] = {
+        "input": deepcopy(input_data),
+        "steps": {},
+    }
+
+    workflow_error: str | None = None
+    run_output: Any = None
+    try:
+        run_output = await workflow.arun(
+            input=input_data,
+            session_state=session_state,
+        )
+    except Exception as exc:
+        workflow_error = str(exc)
+
+    steps_payloads = session_state.get("steps", {}) or {}
+    step_states = _build_step_states(scenario, steps_payloads)
+
+    status = "success"
+    if workflow_error is not None:
+        status = "failed"
+    else:
+        for payload in steps_payloads.values():
+            if isinstance(payload, dict) and not bool(payload.get("ok", False)):
+                status = "failed"
+                break
+        if run_output is not None and not bool(getattr(run_output, "success", True)):
+            status = "failed"
+
+    content = getattr(run_output, "content", None)
+    if isinstance(content, str):
+        try:
+            content = json.loads(content)
+        except json.JSONDecodeError:
+            content = {"raw_content": content}
+    if content is None:
+        for payload in reversed(list(steps_payloads.values())):
+            if isinstance(payload, dict):
+                content = deepcopy(payload)
+                break
+    if content is None and workflow_error is not None:
+        content = {"message": workflow_error}
+
+    result = content if isinstance(content, dict) else {"raw_content": content}
+    response_message = "" if status == "success" else (workflow_error or "failed")
+
+    return ScenarioRunResponse(
+        scenario_id=scenario_id,
+        status=status,
+        message=response_message,
+        input=input_data,
+        steps=step_states,
+        output_summary=_extract_output_summary(result),
+        scenario_name=scenario_name,
+        workflow_name=workflow.name,
+        result=result,
+        run_id=str(getattr(run_output, "run_id", "")) or None,
+        session_id=str(getattr(run_output, "session_id", "")) or None,
+    )

src/observability.py

@@ -0,0 +1,36 @@
+import os
+
+from phoenix.otel import register
+
+_initialized = False
+
+
+def _env_bool(name: str, default: bool) -> bool:
+    value = os.getenv(name)
+    if value is None:
+        return default
+    return value.strip().lower() in {"1", "true", "yes", "on"}
+
+
+def init_phoenix_tracing() -> bool:
+    global _initialized
+
+    enabled = _env_bool("PHOENIX_TRACING_ENABLED", False)
+    if not enabled:
+        return False
+
+    if _initialized:
+        return True
+
+    os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = os.getenv(
+        "PHOENIX_COLLECTOR_ENDPOINT",
+        "http://localhost:6006",
+    )
+
+    project_name = os.getenv("PHOENIX_PROJECT_NAME", "prisma-platform")
+    register(
+        project_name=project_name,
+        auto_instrument=True,
+    )
+    _initialized = True
+    return True

src/scenario_store.py

@@ -0,0 +1,60 @@
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any
+
+
+class ScenarioStoreError(ValueError):
+    """Raised when scenario definitions are missing or invalid."""
+
+
+_SCENARIOS_ROOT = Path(__file__).resolve().parent.parent / "scenarios"
+_INDEX_PATH = _SCENARIOS_ROOT / "index.json"
+
+
+def _read_json(path: Path) -> dict[str, Any]:
+    try:
+        raw = path.read_text(encoding="utf-8")
+    except FileNotFoundError as exc:
+        raise ScenarioStoreError(f"Scenario file not found: {path}") from exc
+
+    try:
+        parsed = json.loads(raw)
+    except json.JSONDecodeError as exc:
+        raise ScenarioStoreError(f"Invalid JSON in file: {path}") from exc
+
+    if not isinstance(parsed, dict):
+        raise ScenarioStoreError(f"JSON root must be object: {path}")
+    return parsed
+
+
+def load_scenario_index() -> dict[str, str]:
+    index = _read_json(_INDEX_PATH)
+    scenarios = index.get("scenarios")
+    if not isinstance(scenarios, dict):
+        raise ScenarioStoreError("index.json must contain object field 'scenarios'")
+
+    normalized: dict[str, str] = {}
+    for scenario_id, relative_path in scenarios.items():
+        if not isinstance(scenario_id, str) or not isinstance(relative_path, str):
+            raise ScenarioStoreError("index.json scenario entries must be string -> string")
+        normalized[scenario_id] = relative_path
+    return normalized
+
+
+def load_scenario_definition(scenario_id: str) -> dict[str, Any]:
+    index = load_scenario_index()
+    relative_path = index.get(scenario_id)
+    if relative_path is None:
+        raise ScenarioStoreError(f"Unknown scenario_id: {scenario_id}")
+
+    scenario_path = _SCENARIOS_ROOT / relative_path
+    scenario = _read_json(scenario_path)
+
+    declared_id = scenario.get("scenario_id")
+    if declared_id != scenario_id:
+        raise ScenarioStoreError(
+            "Scenario file scenario_id does not match requested scenario_id"
+        )
+    return scenario

src/schemas.py

@@ -0,0 +1,35 @@
+from __future__ import annotations
+
+from typing import Any, Literal
+
+from pydantic import BaseModel, Field
+
+RunStatus = Literal["queued", "running", "success", "failed", "waiting_human"]
+StepStatus = Literal["queued", "running", "success", "failed", "waiting_human"]
+
+
+class ScenarioRunRequest(BaseModel):
+    scenario_id: str = "news_source_discovery_v1"
+    input: dict[str, Any] = Field(default_factory=dict)
+
+
+class StepState(BaseModel):
+    node_id: str
+    status: StepStatus
+    started_at: str | None = None
+    finished_at: str | None = None
+    message: str = ""
+
+
+class ScenarioRunResponse(BaseModel):
+    scenario_id: str
+    status: RunStatus
+    message: str = ""
+    input: dict[str, Any]
+    steps: list[StepState] = Field(default_factory=list)
+    output_summary: str | None = None
+    scenario_name: str | None = None
+    workflow_name: str | None = None
+    result: dict[str, Any] | None = None
+    run_id: str | None = None
+    session_id: str | None = None

src/step_planner.py

@@ -0,0 +1,129 @@
+from __future__ import annotations
+
+from copy import deepcopy
+import json
+import os
+from typing import Any
+
+from openai import AsyncOpenAI
+
+
+_planner_client: AsyncOpenAI | None = None
+
+
+def _env_float(name: str, default: float) -> float:
+    value = os.getenv(name)
+    if value is None:
+        return default
+    return float(value)
+
+
+def planner_enabled() -> bool:
+    return os.getenv("PLANNER_ENABLED", "false").strip().lower() in {"1", "true", "yes"}
+
+
+def _get_client() -> AsyncOpenAI:
+    global _planner_client
+    if _planner_client is not None:
+        return _planner_client
+    _planner_client = AsyncOpenAI(
+        base_url=os.getenv("POLZA_BASE_URL", "https://api.polza.ai/v1"),
+        api_key=os.getenv("POLZA_API_KEY") or os.getenv("OPENAI_API_KEY"),
+    )
+    return _planner_client
+
+
+def _response_schema(required_fields: list[str]) -> dict[str, Any]:
+    value_schema = {"type": ["string", "number", "boolean", "array", "object", "null"]}
+    return {
+        "name": "mcp_arguments",
+        "strict": True,
+        "schema": {
+            "type": "object",
+            "properties": {
+                "arguments": {
+                    "type": "object",
+                    "properties": {f: value_schema for f in required_fields},
+                    "required": required_fields,
+                    "additionalProperties": True,
+                }
+            },
+            "required": ["arguments"],
+            "additionalProperties": False,
+        },
+    }
+
+
+def _extract_arguments(content: Any) -> dict[str, Any]:
+    candidate: Any = content
+    if isinstance(candidate, str):
+        text = candidate.strip()
+        if text.startswith("```"):
+            text = text.strip("`").strip()
+            if text.startswith("json"):
+                text = text[4:].strip()
+        try:
+            candidate = json.loads(text)
+        except json.JSONDecodeError:
+            return {}
+    if isinstance(candidate, dict):
+        if isinstance(candidate.get("arguments"), dict):
+            return candidate["arguments"]
+        return candidate
+    return {}
+
+
+async def plan_arguments(
+    *,
+    step_name: str,
+    tool_name: str,
+    base_arguments: dict[str, Any],
+    required_fields: list[str],
+    scope: dict[str, Any],
+    missing_fields: list[str],
+    attempt_no: int,
+) -> dict[str, Any]:
+    """Fallback planner: asks an LLM to fill missing required fields from context.
+
+    Returns merged arguments (base + planned). On any failure returns base_arguments
+    unchanged — caller is responsible for validating required fields afterwards.
+    """
+    prompt = {
+        "task": "Prepare MCP arguments for this step.",
+        "step_name": step_name,
+        "tool_name": tool_name,
+        "required_fields": required_fields,
+        "base_arguments": base_arguments,
+        "missing_fields": missing_fields,
+        "repair_attempt": attempt_no,
+        "context": {"input": scope.get("input", {}), "steps": scope.get("steps", {})},
+        "output": (
+            "Return only JSON object with key 'arguments'. "
+            "Fill every missing field from context."
+        ),
+    }
+    try:
+        completion = await _get_client().chat.completions.create(
+            model=os.getenv("POLZA_MODEL_ID", "google/gemma-4-31b-it"),
+            messages=[
+                {
+                    "role": "system",
+                    "content": (
+                        "You are a tool-input planner. "
+                        "Return only JSON that matches the provided schema."
+                    ),
+                },
+                {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)},
+            ],
+            response_format={"type": "json_schema", "json_schema": _response_schema(required_fields)},
+            temperature=_env_float("POLZA_TEMPERATURE", 0.0),
+        )
+        raw = completion.choices[0].message.content if completion.choices else ""
+        planned = _extract_arguments(raw)
+    except Exception:
+        planned = {}
+
+    merged = deepcopy(base_arguments)
+    if isinstance(planned, dict):
+        merged.update(planned)
+    return merged

src/template.py

@@ -0,0 +1,51 @@
+from __future__ import annotations
+
+from copy import deepcopy
+from typing import Any
+
+
+def resolve_path(scope: dict[str, Any], path: str) -> Any:
+    value: Any = scope
+    for segment in path.split("."):
+        key = segment.strip()
+        if not key:
+            continue
+        if not isinstance(value, dict):
+            return None
+        value = value.get(key)
+    return deepcopy(value)
+
+
+def resolve_template(template: Any, scope: dict[str, Any]) -> Any:
+    if isinstance(template, dict):
+        if set(template.keys()) == {"from"}:
+            return resolve_path(scope, str(template["from"]))
+        return {key: resolve_template(value, scope) for key, value in template.items()}
+    if isinstance(template, list):
+        return [resolve_template(item, scope) for item in template]
+    return deepcopy(template)
+
+
+def missing_required_fields(
+    arguments: dict[str, Any],
+    required_fields: list[str],
+) -> list[str]:
+    missing: list[str] = []
+    for field in required_fields:
+        value = arguments.get(field)
+        if isinstance(value, str) and value.strip():
+            continue
+        if value not in (None, "", [], {}):
+            continue
+        missing.append(field)
+    return missing
+
+
+def validate_required_fields(
+    arguments: dict[str, Any],
+    required_fields: list[str],
+    step_name: str,
+) -> None:
+    missing = missing_required_fields(arguments, required_fields)
+    if missing:
+        raise ValueError(f"{step_name}: missing required fields: {', '.join(missing)}")