How to Use the NLP Cloud MCP in LlamaIndex

Q: How do I register NLP Cloud tools in LlamaIndex?

Initialize a BasicMCPClient with your endpoint URL, wrap it in an McpToolSpec, and call await mcptoolspec.totoollistasync(). Pass that list to your FunctionAgent.

Q: Can LlamaIndex filter which NLP Cloud tools the agent sees?

Yes. You can use the allowedtools parameter to restrict your agent. For example, you might only give it access to summarization and hide the audio tools.

Index entity extraction and sentiment data straight into your LlamaIndex vector stores.

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Connect NLP Cloud MCP to LlamaIndex

Create your Vinkius account to connect NLP Cloud 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 NLP Cloud with LlamaIndex

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Index Output from the MCP Server

The `extract_entities` tool pulls names, dates, and locations out of unstructured text. Your LlamaIndex application takes those structured entities and embeds them directly into a queryable knowledge base. Instead of just throwing tool outputs away, you build a permanent record of your data. When a user asks a question later, the system retrieves answers grounded in the actual entity data it pulled earlier.

Audio Search with LlamaIndex

The `perform_asr` tool converts raw video and audio files into readable transcripts. LlamaIndex then chunks that transcript and indexes it for semantic search. This means you can query past meeting recordings or customer calls exactly like you query text documents. The MCP Server handles the tool execution, and the framework handles the embedding.

Multilingual Document Retrieval

The `translate_text` tool converts foreign language documents into English before indexing. You feed the translated text into your vector store to maintain a single, unified language index. You also run `classify_text` on those same documents to attach metadata tags to your vectors. This lets you filter your semantic searches by category, narrowing down results before the LLM even sees them.

Setup guide

Set up NLP Cloud 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 NLP Cloud 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 NLP Cloud tools.",
)
response = await agent.run("List recent NLP Cloud data")

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Common questions about NLP Cloud MCP in LlamaIndex

Initialize a `BasicMCPClient` with your endpoint URL, wrap it in an `McpToolSpec`, and call `await mcp_tool_spec.to_tool_list_async()`. Pass that list to your FunctionAgent.

Yes. You can use the `allowed_tools` parameter to restrict your agent. For example, you might only give it access to summarization and hide the audio tools.

The framework gives you the translated string. You still need to explicitly add that string to your document store and run the embedding step.

Your FunctionAgent reads the tool schema and constructs the JSON payload automatically. You just prompt the agent with the file location.

The MCP architecture keeps your raw text blocks securely routed through a zero-trust endpoint. The server authenticates via a single token and never writes your documents to persistent storage.

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