shivonai-mcp

Official
by shivonai

Integrations

  • Provides custom hiring tools for CrewAI agents, facilitating recruitment-related tasks and data analysis within CrewAI workflows

  • Enables AI agents to use custom hiring tools through LangChain, allowing integration of recruitment capabilities with LangChain agents

  • Integrates with OpenAI models like GPT-4 to power the AI agent capabilities for recruitment tools and data analysis tasks

ShivonAI

A Python package for integrating AI recruitment tools with various AI agent frameworks.

Features

  • Acess custom hiring tools for AI agents
  • Integrate MCP tools with popular AI agent frameworks:
    • LangChain
    • LlamaIndex
    • CrewAI
    • Agno

Generate auth_token

visit https://shivonai.com to generate your auth_token.

Installation

pip install shivonai[langchain] # For LangChain pip install shivonai[llamaindex] # For LlamaIndex pip install shivonai[crewai] # For CrewAI pip install shivonai[agno] # For Agno pip install shivonai[all] # For all frameworks

Getting Started

LangChain Integration

from langchain_openai import ChatOpenAI from langchain.agents import initialize_agent, AgentType from shivonai.lyra import langchain_toolkit # Replace with your actual MCP server details auth_token = "shivonai_auth_token" # Get LangChain tools tools = langchain_toolkit(auth_token) # Print available tools print(f"Available tools: {[tool.name for tool in tools]}") # Initialize LangChain agent with tools llm = ChatOpenAI( temperature=0, model_name="gpt-4-turbo", openai_api_key="openai-api-key" ) agent = initialize_agent( tools=tools, llm=llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True ) # Try running the agent with a simple task try: result = agent.run("what listing I have?") print(f"Result: {result}") except Exception as e: print(f"Error: {e}")

LlamaIndex Integration

from llama_index.llms.openai import OpenAI from llama_index.core.agent import ReActAgent from shivonai.lyra import llamaindex_toolkit # Set up OpenAI API key - you'll need this to use OpenAI models with LlamaIndex os.environ["OPENAI_API_KEY"] = "openai_api_key" # Your MCP server authentication details MCP_AUTH_TOKEN = "shivonai_auth_token" def main(): """Test LlamaIndex integration with ShivonAI.""" print("Testing LlamaIndex integration with ShivonAI...") # Get LlamaIndex tools from your MCP server tools = llamaindex_toolkit(MCP_AUTH_TOKEN) print(f"Found {len(tools)} MCP tools for LlamaIndex:") for name, tool in tools.items(): print(f" - {name}: {tool.metadata.description[:60]}...") # Create a LlamaIndex agent with these tools llm = OpenAI(model="gpt-4") # Convert tools dictionary to a list tool_list = list(tools.values()) # Create the ReAct agent agent = ReActAgent.from_tools( tools=tool_list, llm=llm, verbose=True ) # Test the agent with a simple query that should use one of your tools # Replace this with a query that's relevant to your tools query = "what listings I have?" print("\nTesting agent with query:", query) response = agent.chat(query) print("\nAgent response:") print(response) if __name__ == "__main__": main()

CrewAI Integration

from crewai import Agent, Task, Crew from langchain_openai import ChatOpenAI # or any other LLM you prefer from shivonai.lyra import crew_toolkit import os os.environ["OPENAI_API_KEY"] = "oepnai_api_key" llm = ChatOpenAI(temperature=0.7, model="gpt-4") # Get CrewAI tools tools = crew_toolkit("shivonai_auth_token") # Print available tools print(f"Available tools: {[tool.name for tool in tools]}") # Create an agent with these tools agent = Agent( role="Data Analyst", goal="Analyze data using custom tools", backstory="You're an expert data analyst with access to custom tools", tools=tools, llm=llm # Provide the LLM here ) # Create a task - note the expected_output field task = Task( description="what listings I have?", expected_output="A detailed report with key insights and recommendations", agent=agent ) crew = Crew( agents=[agent], tasks=[task]) result = crew.kickoff() print(result)

Agno Integration

from agno.agent import Agent from agno.models.openai import OpenAIChat from shivonai.lyra import agno_toolkit import os from agno.models.aws import Claude # Replace with your actual MCP server details auth_token = "Shivonai_auth_token" os.environ["OPENAI_API_KEY"] = "oepnai_api_key" # Get Agno tools tools = agno_toolkit(auth_token) # Print available tools print(f"Available MCP tools: {list(tools.keys())}") # Create an Agno agent with tools agent = Agent( model=OpenAIChat(id="gpt-3.5-turbo"), tools=list(tools.values()), markdown=True, show_tool_calls=True ) # Try the agent with a simple task try: agent.print_response("what listing are there?", stream=True) except Exception as e: print(f"Error: {e}")

License

This project is licensed under a Proprietary License – see the LICENSE file for details.

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security - not tested
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license - not found
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quality - not tested

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Our MCP Tools are designed to enhance AI-driven automated interview services by ensuring a seamless and contextually relevant candidate assessment process. These tools leverage advanced AI models to analyze responses, evaluate competencies, and provide real-time feedback, ma

  1. Features
    1. Generate auth_token
      1. Installation
        1. Getting Started
          1. LangChain Integration
          2. LlamaIndex Integration
          3. CrewAI Integration
          4. Agno Integration
        2. License

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