How to Use the DeepOpinion (No-code NLP & Text AI API) MCP in LlamaIndex

Q: Does this tool specification support resource filtering?

Yes. You can use the allowedtools filter in the tool specification to restrict your agent to specific operations, like only allowing predictions while hiding model listing tools.

Q: Is the text data passed to this MCP Server secure?

Yes. All text sent to predict or predictbatch is processed in memory inside the secure V8 isolate sandbox. The sandbox destroys the execution context immediately after returning the classification results to LlamaIndex.

Index DeepOpinion NLP predictions directly into LlamaIndex vector stores.

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Connect DeepOpinion (No-code NLP & Text AI API) MCP to LlamaIndex

Create your Vinkius account to connect DeepOpinion (No-code NLP & Text AI API) 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 DeepOpinion (No-code NLP & Text AI API) with LlamaIndex

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The `predict` tool analyzes raw text documents and returns classified labels that your LlamaIndex agent writes directly into your vector store. This turns unstructured text files into structured, searchable nodes with accurate semantic metadata. Your agent queries this metadata later to retrieve documents based on their actual NLP classification rather than simple keyword matches. This ensures your search results are grounded in real, classified data.

Scan your custom NLP models dynamically

The `list_models` tool fetches the active taxonomy of your DeepOpinion account so your LlamaIndex agent knows exactly what classification tools are available. Your agent uses this list to match incoming search queries with the correct categorization model. You can update your models in the DeepOpinion dashboard without changing a single line of your indexing code. The agent reads the fresh list on startup and adapts its classification strategy instantly.

Run bulk classifications via an MCP Server

The `predict_batch` tool processes large lists of text chunks simultaneously before indexing them. Your LlamaIndex pipeline feeds raw document nodes into this tool to apply tags across your entire dataset in one pass. This batch approach prevents your indexing script from stalling on individual API requests. You get fully classified metadata for thousands of text nodes in a fraction of the time.

Setup guide

Set up DeepOpinion (No-code NLP & Text AI API) 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 DeepOpinion (No-code NLP & Text AI API) 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 DeepOpinion (No-code NLP & Text AI API) tools.",
)
response = await agent.run("List recent DeepOpinion (No-code NLP & Text AI API) data")

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Common questions about DeepOpinion (No-code NLP & Text AI API) MCP in LlamaIndex

Install llama-index-tools-mcp and instantiate BasicMCPClient with your Vinkius URL. Wrap it in McpToolSpec and call to_tool_list_async to get the tools for your FunctionAgent.

Yes. The text classifications returned by this MCP tool can be attached to LlamaIndex document nodes as metadata. This metadata is then indexed into your vector store, allowing you to filter semantic searches by classification labels.

Your ingestion pipeline can chunk raw documents and pass them directly to the predict_batch tool. This returns classified labels for all chunks at once, which you then insert into your index.

Yes. You can use the allowed_tools filter in the tool specification to restrict your agent to specific operations, like only allowing predictions while hiding model listing tools.

Yes. All text sent to predict or predict_batch is processed in memory inside the secure V8 isolate sandbox. The sandbox destroys the execution context immediately after returning the classification results to LlamaIndex.

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