MonkeyLearn 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 MonkeyLearn 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 MonkeyLearn. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in MonkeyLearn?"
)
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 MonkeyLearn MCP Server
Connect your MonkeyLearn account to your AI agent and leverage powerful NLP models for text analysis and data extraction through natural conversation.
LlamaIndex agents combine MonkeyLearn 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
- Text Classification — Use pre-trained or custom classifiers for sentiment analysis, topic detection, and intent classification.
- Data Extraction — Automatically pull keywords, entities, and specific data points from raw text strings.
- Model Discovery — List and inspect all classifiers, extractors, and pipelines available in your account.
- Workflow Tracking — Monitor your automated workflows and processing activity in real-time.
- Tag Hierarchy — Access the tag trees used by your models to understand classification structures.
- Deep Inspection — Fetch detailed configuration and metadata for specific models using their unique IDs.
The MonkeyLearn 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 MonkeyLearn to LlamaIndex via MCP
Follow these steps to integrate the MonkeyLearn 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 MonkeyLearn
Why Use LlamaIndex with the MonkeyLearn MCP Server
LlamaIndex provides unique advantages when paired with MonkeyLearn through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine MonkeyLearn tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain MonkeyLearn tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query MonkeyLearn, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what MonkeyLearn tools were called, what data was returned, and how it influenced the final answer
MonkeyLearn + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the MonkeyLearn MCP Server delivers measurable value.
Hybrid search: combine MonkeyLearn real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query MonkeyLearn 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 MonkeyLearn for fresh data
Analytical workflows: chain MonkeyLearn queries with LlamaIndex's data connectors to build multi-source analytical reports
MonkeyLearn MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect MonkeyLearn to LlamaIndex via MCP:
classify_text
Classify text using a model
extract_text
Extract data from text
get_classifier_details
Get classifier metadata
get_extractor_details
Get extractor metadata
list_activity
List account activity
list_classifiers
g., sentiment analysis, topic detection) available in your account. List available classifiers
list_extractors
g., keyword extraction, entity recognition) available in your account. List available extractors
list_pipelines
List MonkeyLearn pipelines
list_tag_trees
List available tag trees
list_workflows
List automated workflows
Example Prompts for MonkeyLearn in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with MonkeyLearn immediately.
"Classify the sentiment of this review: 'The product exceeded all my expectations, truly amazing!' using model cl_oZ9GRg8P."
"List all classifiers available in my account."
"Show me my recent processing activity."
Troubleshooting MonkeyLearn MCP Server with LlamaIndex
Common issues when connecting MonkeyLearn to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpMonkeyLearn + LlamaIndex FAQ
Common questions about integrating MonkeyLearn 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 MonkeyLearn 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 MonkeyLearn to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.