How to Use the Winston AI MCP in LlamaIndex

Q: How do I use Winston AI fact-checking within LlamaIndex?

You run the factchecker tool and then pass its findings directly to your indexer. This grounds your entire knowledge base in verifiable claims, dramatically reducing hallucinations.

Q: Does Winston AI help with academic integrity in LlamaIndex?

Yes. You index the results from checkplagiarismfile and use them to build a continuous, searchable record of content risk. The system remembers which documents need review.

Q: Can Winston AI detect plagiarism across multiple sources in LlamaIndex?

You can run the checkplagiarism tools and index every result independently. Then, you query the entire corpus to find patterns of academic overlap across different articles.

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Setup Winston AI with LlamaIndex

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Indexing Plagiarism Results

You can build an index where the output of `check_plagiarism_text` becomes a searchable memory. Instead of just getting a score, you store that finding—the source text and its associated risk level—in your vector store. This means if you run checks across hundreds of articles, you don't lose data. You query the index later to ask, 'Which documents flagged plagiarism last month?' and get answers grounded in actual `check_plagiarism_*` API results.

RAG for Fact Verification

Want your AI agent to only use verified data? Use the `fact_checker` tool, then index its output. The RAG system will then ground future answers against these verified facts. The developer combines live API calls—like running `fact_checker_url` on a specific webpage—and turns that temporary result into permanent, queryable knowledge for your application.

Semantic AI Detection Search

This MCP Server helps build deep search capabilities. After running `detect_ai_text`, you index the detection score and the associated text snippet. Later, you can perform a semantic search on that knowledge base. Instead of just searching for 'AI content,' you ask, 'Show me all texts scored highly for human origin last week,' retrieving structured data from `detect_ai_*` calls.

Setup guide

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

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Common questions about Winston AI MCP in LlamaIndex

You run the `fact_checker` tool and then pass its findings directly to your indexer. This grounds your entire knowledge base in verifiable claims, dramatically reducing hallucinations.

The server handles text strings, URLs pointing to web pages or documents, and dedicated document files. All these results are indexed as chunks of knowledge that your RAG application can retrieve.

Yes. You index the results from `check_plagiarism_file` and use them to build a continuous, searchable record of content risk. The system remembers which documents need review.

You can run the `check_plagiarism_*` tools and index every result independently. Then, you query the entire corpus to find patterns of academic overlap across different articles.

Since you are indexing results, this server primarily touches textual content and file URLs. The index stores the analysis outputs, not the raw source material itself, maintaining a high level of separation.

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