Observe.AI MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Observe.AI as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Observe.AI. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Observe.AI?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Observe.AI MCP Server
Connect your Observe.AI account to your AI agent and gain deep visibility into your contact center performance and conversation intelligence through natural conversation.
LlamaIndex agents combine Observe.AI tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Interaction Monitoring — List and inspect all calls, chats, and emails processed by the platform, including metadata and analysis.
- Full Transcripts — Retrieve the complete text transcripts for any call or chat interaction for detailed review.
- QA & Evaluations — Access quality assurance scores, evaluation forms, and individual agent performance metrics.
- AI Insights — View automated interaction summaries and identified business moments (e.g., Greetings, Objections).
- Coaching Oversight — Monitor agent coaching sessions and feedback logs to track improvement.
- Workspace Management — List all agents, supervisors, and admins in your Observe.AI instance.
The Observe.AI MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Observe.AI to LlamaIndex via MCP
Follow these steps to integrate the Observe.AI MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Observe.AI
Why Use LlamaIndex with the Observe.AI MCP Server
LlamaIndex provides unique advantages when paired with Observe.AI through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Observe.AI tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Observe.AI tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Observe.AI, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Observe.AI tools were called, what data was returned, and how it influenced the final answer
Observe.AI + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Observe.AI MCP Server delivers measurable value.
Hybrid search: combine Observe.AI real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Observe.AI to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Observe.AI for fresh data
Analytical workflows: chain Observe.AI queries with LlamaIndex's data connectors to build multi-source analytical reports
Observe.AI MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Observe.AI to LlamaIndex via MCP:
get_evaluation_details
Get specific evaluation info
get_interaction_details
Get specific interaction info
get_interaction_transcript
Get interaction transcript
list_coaching_sessions
List agent coaching sessions
list_evaluation_forms
List QA evaluation forms
list_interaction_moments
g. Greeting, Closing) across interactions. List identified key moments
list_interaction_summaries
List AI-generated summaries
list_interactions
AI. List contact center interactions
list_qa_evaluations
List QA evaluations
list_workspace_users
AI workspace. List workspace agents and users
Example Prompts for Observe.AI in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Observe.AI immediately.
"List all recent call interactions from today."
"What is the QA score for interaction ID 'int_12345'?"
"Show me the AI summaries for our latest interactions."
Troubleshooting Observe.AI MCP Server with LlamaIndex
Common issues when connecting Observe.AI to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpObserve.AI + LlamaIndex FAQ
Common questions about integrating Observe.AI MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Observe.AI with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Observe.AI to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.