Add CrewAI Integration Example (#56)

* CrewAI example

* feat: add CrewAI integration example

* Bump pydantic

Author: plutasnyy

CHANGELOG.md

@@ -1,3 +1,10 @@
+## 0.1.7-dev0
+
+### Enhancements
+- Added example how to configure MCP server in CrewAI framework
+
+### Fixes
+
 ## 0.1.6
 
 ### Enhancements

example_clients/crew_ai_agent.py

@@ -0,0 +1,102 @@
+#!/usr/bin/env python3
+# /// script
+# dependencies = [
+#   "crewai>=0.11.0",
+#   "crewai-tools[mcp]>=0.0.5",
+#   "pydantic>=2.11.0",
+#   "python-dotenv>=1.0.0",
+# ]
+# ///
+
+"""
+Example script demonstrating how to use CrewAI with Unstructured MCP to configure sources.
+This script creates a CrewAI agent that connects to the Unstructured MCP server
+and configures data sources.
+
+1. Configure UNSTRUCTURED_API_KEY, ANTHROPIC_API_KEY, AWS_KEY, AWS_SECRET keys in .env file
+2. Start MCP server with:
+```
+make sse-server
+```
+
+3. Run example with:
+```
+uv run example_clients/crew_ai_agent.py
+```
+"""
+
+import os
+from typing import Optional
+
+from crewai import LLM, Agent, Crew, Task
+from crewai_tools import MCPServerAdapter
+from dotenv import load_dotenv
+from pydantic import BaseModel, Field
+from rich.pretty import pprint
+
+load_dotenv()
+
+
+class SourceConfigurationResult(BaseModel):
+    source_id: Optional[str] = Field(
+        default=None,
+        description="The ID of the configured data source",
+    )
+    source_type: Optional[str] = Field(
+        default=None,
+        description="The type of data source configured",
+    )
+    source_name: Optional[str] = Field(
+        default=None,
+        description="The name of the configured data source",
+    )
+    source_config: Optional[dict] = Field(
+        default=None,
+        description="The configuration details of the data source",
+    )
+
+
+def main():
+
+    with MCPServerAdapter(
+        {"url": os.getenv("MCP_SERVER_URL", "http://127.0.0.1:8080/sse")},
+    ) as tools:
+        llm = LLM(model="anthropic/claude-3-opus-20240229", temperature=0.7, max_tokens=4096)
+
+        agent = Agent(
+            role="Source Configuration Specialist",
+            goal="Configure and manage data sources for the MCP server",
+            backstory="""You are an expert in data source configuration and management.
+            You specialize in setting up and configuring various types of data sources
+            including AWS S3, Google Drive, and other storage systems. You ensure
+            proper configuration and validation of data sources.""",
+            tools=tools,
+            llm=llm,
+            verbose=True,
+        )
+
+        task = Task(
+            description="""Configure an S3 source with the following specifications:
+            - Name: MCP-S3-Source
+            - URI: s3://test/uri
+            - Recursive: true
+            Ensure the source is properly configured and return the configuration details.""",
+            agent=agent,
+            expected_output="""A result containing:
+            - source_id: The ID of the configured source
+            - source_config: The configuration details
+            - source_name: The name of the configured source
+            - source_type: The type of data source configured
+            """,
+            output_pydantic=SourceConfigurationResult,
+        )
+
+        crew = Crew(agents=[agent], tasks=[task], verbose=True)
+
+        result = crew.kickoff()
+        pprint("Task Result:")
+        pprint(result.tasks_output[0].pydantic)
+
+
+if __name__ == "__main__":
+    main()

uns_mcp/connectors/source/source_tool.py

@@ -28,7 +28,6 @@ async def create_source_connector(
     type_specific_config: dict[str, Any],
 ) -> str:
     """Create a source connector based on type.
-
     Args:
         ctx: Context object with the request and lifespan context
         name: A unique name for this connector