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Pietro Zullo
e6ba394ec7
- Format 3 adapter files to match ruff 0.15 (CI version) - Remove the PR comment step from docs.yml that spammed every push
114 lines
4.2 KiB
Python
114 lines
4.2 KiB
Python
import re
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from collections.abc import Callable, Coroutine
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from typing import Any
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from mcp.types import Prompt, Resource, Tool
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from mcp_use.agents.adapters.base import BaseAdapter
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from mcp_use.client.connectors.base import BaseConnector
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def _sanitize_for_tool_name(name: str) -> str:
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"""Sanitizes a string to be a valid tool name for OpenAI."""
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# OpenAI tool names can only contain a-z, A-Z, 0-9, and underscores,
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# and must be 64 characters or less.
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return re.sub(r"[^a-zA-Z0-9_]+", "_", name).strip("_")[:64]
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class OpenAIMCPAdapter(BaseAdapter[dict[str, Any]]):
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framework: str = "openai"
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def __init__(self, disallowed_tools: list[str] | None = None) -> None:
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"""Initialize a new OpenAI adapter.
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Args:
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disallowed_tools: list of tool names that should not be available.
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"""
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super().__init__(disallowed_tools=disallowed_tools)
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# This map stores the actual async function to call for each tool.
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self.tool_executors: dict[str, Callable[..., Coroutine[Any, Any, Any]]] = {}
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self._connector_tool_map: dict[BaseConnector, list[dict[str, Any]]] = {}
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self._connector_resource_map: dict[BaseConnector, list[dict[str, Any]]] = {}
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self._connector_prompt_map: dict[BaseConnector, list[dict[str, Any]]] = {}
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self.tools: list[dict[str, Any]] = []
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self.resources: list[dict[str, Any]] = []
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self.prompts: list[dict[str, Any]] = []
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def _convert_tool(self, mcp_tool: Tool, connector: BaseConnector) -> dict[str, Any] | None:
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"""Convert an MCP tool to the OpenAI tool format."""
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if mcp_tool.name in self.disallowed_tools:
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return None
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self.tool_executors[mcp_tool.name] = lambda connector=connector, name=mcp_tool.name, **kwargs: (
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connector.call_tool(name, kwargs)
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)
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fixed_schema = self.fix_schema(mcp_tool.inputSchema)
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return {
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"type": "function",
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"function": {
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"name": mcp_tool.name,
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"description": mcp_tool.description,
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"parameters": fixed_schema,
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},
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}
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def _convert_resource(self, mcp_resource: Resource, connector: BaseConnector) -> dict[str, Any] | None:
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"""Convert an MCP resource to a readable tool in OpenAI format."""
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# Sanitize the name to be a valid function name for OpenAI
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tool_name = _sanitize_for_tool_name(f"resource_{mcp_resource.name}")
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if tool_name in self.disallowed_tools:
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return None
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self.tool_executors[tool_name] = lambda connector=connector, uri=mcp_resource.uri, **kwargs: (
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connector.read_resource(uri)
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)
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mcp_resource_desc = mcp_resource.description
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return {
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"type": "function",
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"function": {
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"name": tool_name,
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"description": mcp_resource_desc,
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# They take no arguments
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"parameters": {"type": "object", "properties": {}},
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},
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}
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def _convert_prompt(self, mcp_prompt: Prompt, connector: BaseConnector) -> dict[str, Any] | None:
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"""Convert an MCP prompt to a usable tool in OpenAI format."""
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if mcp_prompt.name in self.disallowed_tools:
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return None
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self.tool_executors[mcp_prompt.name] = lambda connector=connector, name=mcp_prompt.name, **kwargs: (
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connector.get_prompt(name, kwargs)
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)
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# Preparing JSON schema for prompt arguments
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properties = {}
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required_args = []
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if mcp_prompt.arguments:
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for arg in mcp_prompt.arguments:
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prop = {"type": "string"}
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if arg.description:
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prop["description"] = arg.description
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properties[arg.name] = prop
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if arg.required:
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required_args.append(arg.name)
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parameters_schema = {"type": "object", "properties": properties}
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if required_args:
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parameters_schema["required"] = required_args
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return {
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"type": "function",
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"function": {
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"name": mcp_prompt.name,
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"description": mcp_prompt.description,
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"parameters": parameters_schema,
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},
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}
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