mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-08-01 08:30:20 +00:00
24f816b6a3
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
107 lines
3.6 KiB
Python
107 lines
3.6 KiB
Python
#
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# Copyright 2026 The Dapr Authors
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import logging
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from typing import Any, Dict, Optional
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from pydantic import BaseModel, Field
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from dapr_agents.tool.workflow.tool_context import WorkflowContextInjectedTool
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logger = logging.getLogger(__name__)
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def make_mcp_gateway_via_child_workflow_tool(
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*,
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target_app_id: str,
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gateway_workflow_name: str,
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name: str = "McpGatewayCall",
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) -> WorkflowContextInjectedTool:
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"""
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Create an AgentTool that calls an MCP "gateway workflow" hosted in another Dapr app,
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using the *current* workflow context (passed in by the agent) to schedule a
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multi-app child workflow.
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This uses Dapr's multi-application child workflow routing via `app_id`.
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The called workflow is expected to:
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- Receive input: {"tool": <str>, "arguments": <dict>}
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- Return: a JSON-serializable result (string/dict/etc.) representing the MCP call result.
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"""
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class Args(BaseModel):
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tool: str = Field(
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..., description="Name of the MCP tool to call on the remote side."
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)
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arguments: Dict[str, Any] = Field(
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default_factory=dict,
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description="Arguments to pass to the remote MCP tool.",
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)
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instance_id: Optional[str] = Field(
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default=None,
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description=(
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"Optional child workflow instance id. Use when you need idempotency / "
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"dedupe semantics at the child-workflow level."
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),
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)
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def _executor(
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ctx: Any,
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tool: str,
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arguments: Dict[str, Any],
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instance_id: Optional[str] = None,
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) -> Any:
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"""
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Schedule a child workflow on the target app id, passing the MCP tool call as input.
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NOTE: This is intentionally a *sync* function that returns a workflow Task.
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In a Dapr workflow orchestrator, you'd typically do:
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result = yield tool_obj(ctx=ctx, tool="X", arguments={...})
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"""
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payload = {"tool": tool, "arguments": arguments}
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logger.debug(
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"Scheduling child workflow '%s' on app_id='%s' for MCP tool='%s' args=%s instance_id=%s",
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gateway_workflow_name,
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target_app_id,
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tool,
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arguments,
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instance_id,
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)
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# Dapr multi-app child workflow call: route execution to target_app_id.
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# API: yield ctx.call_child_workflow(workflow='Workflow2', input='my-input', app_id='App2')
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if instance_id:
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return ctx.call_child_workflow(
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workflow=gateway_workflow_name,
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input=payload,
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instance_id=instance_id,
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app_id=target_app_id,
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)
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return ctx.call_child_workflow(
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workflow=gateway_workflow_name,
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input=payload,
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app_id=target_app_id,
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)
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return WorkflowContextInjectedTool(
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name=name,
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description=(
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f"Gateway tool that calls MCP via child workflow '{gateway_workflow_name}' "
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f"hosted by Dapr app '{target_app_id}'."
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),
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func=_executor,
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args_model=Args,
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)
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