How to Use the Password Strength Evaluator MCP in LlamaIndex

Q: Are the analyzed password strings stored by the server?

No, the raw strings passed to evaluatepassword are processed in memory and immediately discarded. The server operates in an ephemeral MCP sandbox, leaving zero trace of the analyzed credentials.

Index password audit results directly into LlamaIndex vector stores to spot systemic credential vulnerabilities.

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Connect Password Strength Evaluator MCP to LlamaIndex

Create your Vinkius account to connect Password Strength Evaluator to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Password Strength Evaluator with LlamaIndex

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Index credential entropy metrics in LlamaIndex

The `evaluate_password` tool allows your LlamaIndex pipeline to analyze password strength locally and store those metrics directly in your vector database. By feeding raw passwords into this tool, your agent gets back a precise score from 0 to 4 along with estimated offline crack times. Because this structured output is returned instantly, it can be converted into nodes for semantic indexing. You can then run complex queries over your entire indexed audit history. Instead of guessing your risk profile, you query the index to find how many accounts rely on weak, easily cracked patterns. It turns raw security checks into a searchable knowledge base.

Ground your security audits in actual entropy data

Running the `evaluate_password` tool inside your RAG workflows ensures your agent's security recommendations are grounded in mathematical reality rather than LLM hallucinations. The tool uses Dropbox's zxcvbn engine to calculate spatial patterns and dictionary matches on the fly. Your agent reads these concrete metrics to construct its response. When users ask why their proposed password was rejected, the agent pulls the exact feedback from the tool output. It explains the vulnerability using real crack-time estimates. This keeps your user-facing security advice accurate and helpful.

Build queryable audit trails with this MCP Server

The `evaluate_password` tool drives automated bulk credential auditing inside your LlamaIndex FunctionAgent workflows. Your agent calls the tool for every account in your target list, compiling scores and spatial warnings without manual scripting. This automated process runs entirely locally to preserve data privacy. Once the run completes, you can query the resulting index to spot trends, like common dictionary words used across departments. It gives you a clear bird's-eye view of your organizational security posture. You get actionable intelligence without writing complex parser scripts.

Setup guide

Set up Password Strength Evaluator 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 Password Strength Evaluator 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 Password Strength Evaluator tools.",
)
response = await agent.run("List recent Password Strength Evaluator data")

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Common questions about Password Strength Evaluator MCP in LlamaIndex

Install llama-index-tools-mcp and initialize the BasicMCPClient. Convert the client tools using McpToolSpec and pass them to your FunctionAgent to start calling `evaluate_password`.

Yes, the tool outputs structured JSON containing scores and crack times. LlamaIndex can ingest these results directly into documents and index them in a vector store for semantic querying.

Your agent loops through your user list using the MCP connection, calls `evaluate_password` on each record, and aggregates the scores. The results are stored locally, giving you a completely private bulk audit.

No, the zxcvbn engine runs completely locally. Your LlamaIndex agent can evaluate passwords in air-gapped environments without leaking any data.

No, the raw strings passed to `evaluate_password` are processed in memory and immediately discarded. The server operates in an ephemeral MCP sandbox, leaving zero trace of the analyzed credentials.

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