How to Use the MonkeyLearn MCP in LlamaIndex

Q: How do I install the MonkeyLearn MCP Server spec for LlamaIndex?

First, run pip install llama-index-tools-mcp to get the necessary package. Then configure the client with your Vinkius URL and convert the tools to use with your FunctionAgent.

Q: Can LlamaIndex use MonkeyLearn to filter search results?

Yes, you can use the output of classifytext as metadata filters. This allows your query engine to restrict searches to documents that match specific sentiment or category tags.

Index text classifications and extracted keywords directly into your LlamaIndex vector store.

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Connect MonkeyLearn MCP to LlamaIndex

Create your Vinkius account to connect MonkeyLearn to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup MonkeyLearn with LlamaIndex

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Enrich LlamaIndex vector queries with metadata

This MCP Server lets your LlamaIndex agent tag documents before indexing them. The agent runs `classify_text` on incoming documents, then stores the resulting categories as metadata in your vector index. Searching your index becomes much more precise. You can filter vector queries by the exact sentiment or topic tags retrieved from `list_classifiers`.

Extract and index key entities from raw files

Stop indexing noisy, unformatted text. Your agent can run `extract_text` to pull out keywords, names, or product codes from raw documents before they hit your database. This step ensures your index stays clean. By checking `get_extractor_details`, the agent understands what specific entities it can extract to keep search results relevant.

Keep your indexing pipeline synchronized

Your agent needs to know what extraction models are active. Running `list_extractors` lets the agent verify your custom models are ready to process incoming files. If you use complex taxonomies, `list_tag_trees` provides the exact structure. Your agent uses this live structure to map new documents to the correct nodes in your knowledge graph.

Setup guide

Set up MonkeyLearn MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all MonkeyLearn MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to MonkeyLearn tools.",
)
response = await agent.run("List recent MonkeyLearn data")

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Common questions about MonkeyLearn MCP in LlamaIndex

First, run `pip install llama-index-tools-mcp` to get the necessary package. Then configure the client with your Vinkius URL and convert the tools to use with your FunctionAgent.

Yes, you can use the output of `classify_text` as metadata filters. This allows your query engine to restrict searches to documents that match specific sentiment or category tags.

It supports any custom model in your account. The agent can call `list_extractors` to find your custom models, then use `extract_text` with the specific model ID.

You can analyze individual text chunks before storing them. Your agent runs `classify_text` on each chunk to determine if it contains relevant information or noise.

The server handles document text strings solely to run classification and extraction. All data transmissions are encrypted, and the Vinkius runtime uses ephemeral sandboxes that discard all request data immediately after the API call finishes.

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