How to Use the BrightMove MCP in LlamaIndex

Q: How do I index BrightMove data using LlamaIndex?

Use the LlamaIndex MCP tool spec to fetch data via listapplicants and convert the results into Document objects for your vector store.

Q: Can I use the BrightMove MCP server for semantic candidate search in LlamaIndex?

Yes. By indexing the output of getapplicant into LlamaIndex, you can run semantic search queries to find candidates with specific skills.

Q: How does the BrightMove MCP server secure candidate data?

All data retrieved from tools like listreferences is processed in memory or stored in your own local vector database. Vinkius does not log or persist your sensitive recruitment data.

Index your recruitment pipeline into LlamaIndex to query candidate data with zero hallucinations.

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Connect BrightMove MCP to LlamaIndex

Create your Vinkius account to connect BrightMove 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 BrightMove with LlamaIndex

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Build RAG Indexes over BrightMove Data

`list_applicants` pulls your entire candidate pool into LlamaIndex's vector storage. This MCP server lets you turn raw applicant profiles into searchable, indexed knowledge documents. Once indexed, your agent queries candidate history without hitting the API repeatedly. It matches candidate resumes with open positions by comparing vector embeddings of your job descriptions.

Query Active Jobs and Company Specs in LlamaIndex

`get_job` retrieves specific job requirements to ground your agent's reasoning. Your agent uses this data alongside `get_company_info` to answer candidate questions accurately. This prevents the model from hallucinating salary ranges or role requirements. The agent retrieves the exact text from the active index to keep responses factual.

Track Placements and Team Performance

`list_placements` feeds your historical hiring success data directly into your query engine. The agent analyzes these records to find patterns in successful hires. You can also query `list_user_accounts` to see which recruiters closed those placements. This turns your operational logs into a structured knowledge base for team performance.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by BrightMove. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about BrightMove MCP in LlamaIndex

Use the LlamaIndex MCP tool spec to fetch data via `list_applicants` and convert the results into Document objects for your vector store.

Yes. By indexing the output of `get_applicant` into LlamaIndex, you can run semantic search queries to find candidates with specific skills.

You control the caching layer. You can index the data once or configure the agent to call active tools like `get_recruitment_dashboard` for live updates.

Yes. LlamaIndex allows you to pass an allowed tools list, restricting the agent to specific operations like viewing jobs while blocking placement data.

All data retrieved from tools like `list_references` is processed in memory or stored in your own local vector database. Vinkius does not log or persist your sensitive recruitment data.

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