MonkeyLearn MCP Server for LlamaIndexGive LlamaIndex instant access to 12 tools to Classify Text, Extract Text Entities, Get Api Status, and more
GDPR Free for SubscribersLlamaIndex 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 for LlamaIndex
The MonkeyLearn MCP Server for LlamaIndex is a standout in the Customer Support category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
Geminiimport 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 12 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 any AI agent and run NLP text analysis through natural conversation.
LlamaIndex agents combine MonkeyLearn tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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 — Classify text by sentiment, topic, intent, or custom labels
- Entity Extraction — Pull structured data like names, keywords, and addresses from text
- NLP Workflows — Run multi-step Studio workflows for complex pipelines
- Model Management — List classifiers, extractors, model versions, and tags
- Account Status — Verify API connectivity
The MonkeyLearn MCP Server exposes 12 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 12 MonkeyLearn tools available for LlamaIndex
When LlamaIndex connects to MonkeyLearn through Vinkius, your AI agent gets direct access to every tool listed below — spanning text-classification, entity-extraction, sentiment-analysis, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.
Classify text on MonkeyLearn
Classify text data
Extract text entities on MonkeyLearn
Extract entities
Get api status on MonkeyLearn
Get account status
Get classifier details on MonkeyLearn
Get classifier info
Get extractor details on MonkeyLearn
Get extractor info
List classifier tags on MonkeyLearn
List model tags
List classifiers on MonkeyLearn
List text classifiers
List extractor tags on MonkeyLearn
List extractor tags
List extractors on MonkeyLearn
List text extractors
List model versions on MonkeyLearn
List model versions
List nlp workflows on MonkeyLearn
List account workflows
Run workflow on MonkeyLearn
Run NLP workflow
Connect MonkeyLearn to LlamaIndex via MCP
Follow these steps to wire MonkeyLearn into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
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
Example Prompts for MonkeyLearn in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with MonkeyLearn immediately.
"Classify this customer review: 'The product is amazing but delivery was slow.'"
"Extract entities from: 'John Smith from Apple Inc. visited our NYC office on March 15.'"
"List all my classifiers and extractors."
Troubleshooting MonkeyLearn MCP Server with LlamaIndex
Common issues when connecting MonkeyLearn to LlamaIndex through 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?
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