A Python SDK for seamless integration of MCP (Model Context Protocol) tools with multiple LLM providers including Anthropic Claude, OpenAI GPT, and Google Gemini.
Configure as many MCP servers/tools as you need at observee.ai
# Basic installation
pip install observee-agents
# With optional dependencies
pip install observee-agents[embedding,cloud]
# Development installation
pip install observee-agents[dev]
from observee_agents import chat_with_tools
result = chat_with_tools(
message="Search for recent news about AI developments",
provider="anthropic",
model="claude-sonnet-4-20250514",
observee_api_key="obs_your_key_here"
)
print("Response:", result["content"])
print("Tools used:", len(result["tool_calls"]))
from observee_agents import list_tools, get_tool_info, filter_tools
# List all available tools
tools = list_tools(observee_api_key="obs_your_key_here")
print(f"Found {len(tools)} tools:")
for tool in tools[:5]: # Show first 5
print(f"- {tool['name']}: {tool['description']}")
# Get detailed info about a specific tool
tool_info = get_tool_info(
tool_name="youtube_get_transcript",
observee_api_key="obs_your_key_here"
)
if tool_info:
print(f"Tool: {tool_info['name']}")
print(f"Description: {tool_info['description']}")
# Find relevant tools for a task
relevant_tools = filter_tools(
query="search YouTube videos",
max_tools=3,
observee_api_key="obs_your_key_here"
)
for tool in relevant_tools:
print(f"- {tool['name']} (relevance: {tool['relevance_score']})")
from observee_agents import execute_tool
# Execute a tool directly without LLM
result = execute_tool(
tool_name="youtube_get_transcript",
tool_input={"video_url": "https://youtube.com/watch?v=dQw4w9WgXcQ"},
observee_api_key="obs_your_key_here"
)
print(result)
import asyncio
from observee_agents import chat_with_tools_stream
async def stream_example():
async for chunk in chat_with_tools_stream(
message="What's the weather like today?",
provider="openai",
observee_api_key="obs_your_key_here"
):
if chunk["type"] == "content":
print(chunk["content"], end="", flush=True)
elif chunk["type"] == "tool_result":
print(f"\n[Tool executed: {chunk['tool_name']}]")
asyncio.run(stream_example())
import asyncio
from observee_agents import MCPAgent
async def advanced_example():
async with MCPAgent(
provider="anthropic",
server_url="wss://mcp.observee.ai/mcp?client_id=your_id",
auth_token="obs_your_key_here"
) as agent:
result = await agent.chat_with_tools(
message="What tools do you have access to?"
)
return result
result = asyncio.run(advanced_example())
print(result["content"])
The SDK includes built-in OAuth flows for authenticating with various services:
from observee_agents import call_mcpauth_login, get_available_servers
# Get list of supported authentication servers
servers = get_available_servers()
print(f"Available servers: {servers['supported_servers']}")
# Start authentication flow for Gmail
response = call_mcpauth_login(auth_server="gmail")
print(f"Visit this URL to authenticate: {response['url']}")
# Start authentication flow for Slack with client ID
response = call_mcpauth_login(
auth_server="slack"
)
Supported Services: Gmail, Google Calendar, Google Docs, Google Drive, Google Sheets, Slack, Notion, Linear, Asana, Outlook, OneDrive, Atlassian, Supabase, Airtable, Discord, and more.
# Option 1: API Key (Recommended)
export OBSERVEE_API_KEY="obs_your_key_here"
export OBSERVEE_CLIENT_ID="your_client_id" # Optional
# Option 2: Direct URL
export OBSERVEE_URL="https://mcp.observee.ai/mcp"
# LLM Provider Keys
export ANTHROPIC_API_KEY="your_anthropic_key"
export OPENAI_API_KEY="your_openai_key"
export GOOGLE_API_KEY="your_google_key"
from observee_agents import chat_with_tools
result = chat_with_tools(
message="Your query here",
# Provider Configuration
provider="anthropic", # "anthropic", "openai", "gemini"
model="claude-sonnet-4-20250514", # Auto-detected if not provided
# Authentication (priority: params > env vars)
observee_api_key="obs_your_key",
observee_url="https://custom.mcp.server/endpoint",
client_id="your_client_id",
# Tool Filtering
enable_filtering=True, # True for filtered tools, False for all tools
filter_type="bm25", # "bm25", "local_embedding", "cloud"
max_tools=20, # Maximum tools to filter
min_score=8.0, # Minimum relevance score
# Performance
sync_tools=False, # True to clear caches and resync
# Provider-specific args
temperature=0.7,
max_tokens=1000
)
# Main chat functionality
from observee_agents import chat_with_tools, chat_with_tools_stream
# Tool exploration and management
from observee_agents import list_tools, get_tool_info, filter_tools, execute_tool
# Advanced usage
from observee_agents import MCPAgent
from observee_agents import chat_with_tools
# Anthropic Claude
result = chat_with_tools(
message="Analyze this YouTube video",
provider="anthropic",
model="claude-sonnet-4-20250514"
)
# OpenAI GPT
result = chat_with_tools(
message="Search for recent AI papers",
provider="openai",
model="gpt-4o"
)
# Google Gemini
result = chat_with_tools(
message="Help me manage my emails",
provider="gemini",
model="gemini-2.5-pro"
)
from observee_agents import chat_with_tools
# Fast BM25 keyword filtering (default)
result = chat_with_tools(
message="Find relevant tools",
filter_type="bm25",
max_tools=5
)
# Semantic embedding filtering
result = chat_with_tools(
message="Find relevant tools",
filter_type="local_embedding",
max_tools=10
)
# Cloud hybrid search (requires API keys)
result = chat_with_tools(
message="Find relevant tools",
filter_type="cloud",
max_tools=15
)
# No filtering - use all available tools
result = chat_with_tools(
message="What can you do?",
enable_filtering=False
)
from observee_agents import chat_with_tools
# Custom Observee server
result = chat_with_tools(
message="Custom server query",
observee_url="https://your-custom-server.com/mcp",
client_id="custom_client_123"
)
# Force cache refresh
result = chat_with_tools(
message="Get fresh results",
sync_tools=True # Clears caches
)
{
"content": "The AI response text",
"tool_calls": [
{
"name": "tool_name",
"input": {"param": "value"}
}
],
"tool_results": [
{
"tool": "tool_name",
"result": "tool output"
}
],
"filtered_tools_count": 5,
"filtered_tools": ["tool1", "tool2", "tool3"],
"used_filtering": True
}
The SDK provides access to various MCP tools including:
fastembedpinecone-client, openaiPINECONE_API_KEY, OPENAI_API_KEY# Clone and install in development mode
git clone https://github.com/observee-ai/mcp-agent-system.git #coming soon
cd mcp-agent-system
pip install -e .[dev]
# Run tests
pytest
# Format code
black observee_agents/
All rights reserved. This software is proprietary and confidential. Unauthorized copying, distribution, or use is strictly prohibited.
FAQs
A Python SDK for MCP tool integration with LLM providers
We found that oauth-mcp demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
The EU Cyber Resilience Act is prompting compliance requests that open source maintainers may not be obligated or equipped to handle.
Security News
Crates.io adds Trusted Publishing support, enabling secure GitHub Actions-based crate releases without long-lived API tokens.
Research
/Security News
Undocumented protestware found in 28 npm packages disrupts UI for Russian-language users visiting Russian and Belarusian domains.