How to Use the JobScore MCP in LlamaIndex

Q: Can I use LlamaIndex to query JobScore candidate statuses without hallucinating?

Yes, by calling getcandidate directly through the server, LlamaIndex feeds the exact application status into the LLM context. This guarantees the model's answers are grounded in real-time data.

Q: Does the LlamaIndex JobScore MCP Server integration support semantic search over jobs?

Yes, you can fetch job descriptions using getjob and index them into a vector store. Users can then run semantic queries to find matching roles based on skills or experience.

Q: How do I filter which JobScore tools are exposed to LlamaIndex?

Use the allowedtools filter when setting up your tool specification in LlamaIndex. This lets you restrict your agent to safe tools like listlocations while blocking write operations if needed.

Index live JobScore candidate and job data into LlamaIndex vector stores for RAG-driven hiring.

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Connect JobScore MCP to LlamaIndex

Create your Vinkius account to connect JobScore 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 JobScore with LlamaIndex

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Turn Live Job Listings into Queryable Context

The `list_jobs` tool retrieves your active positions so LlamaIndex can index them directly into your local vector database. This lets your LlamaIndex agent query your open roles semantically, matching user prompts against actual job titles and departments. Instead of reading stale PDFs, your RAG application queries the live JobScore API. When a user asks about engineering roles, LlamaIndex pulls the latest requirements using `get_job` to ground its response in current data.

Index Candidate Profiles for Semantic Search

The `list_candidates` tool fetches your current applicant pool, enabling LlamaIndex to build a searchable knowledge base of your talent pipeline. Your LlamaIndex agent queries this index to find candidates at specific application stages. By fetching detailed profiles with `get_candidate`, your system indexes contact histories and application statuses. This avoids model hallucinations by forcing the LlamaIndex agent to cite actual candidate records in its responses.

Map Hiring Structures using the MCP Server

The `list_departments` tool provides the organizational structure needed to segment your indexed JobScore recruiting data. Your LlamaIndex agent uses this structure to filter search queries, ensuring it only searches within the correct business units. Combining this with `list_hiring_teams` lets your LlamaIndex agent associate specific recruiters with indexed candidate documents. It creates a unified LlamaIndex knowledge graph linking jobs, departments, and hiring managers together.

Setup guide

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

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Common questions about JobScore MCP in LlamaIndex

Initialize the MCP client with your Vinkius URL and wrap it in an McpToolSpec. Pass these tools to your agent, which can run lookups like `list_candidates` and index the raw text output.

Yes, by calling `get_candidate` directly through the server, LlamaIndex feeds the exact application status into the LLM context. This guarantees the model's answers are grounded in real-time data.

Yes, you can fetch job descriptions using `get_job` and index them into a vector store. Users can then run semantic queries to find matching roles based on skills or experience.

Use the allowed_tools filter when setting up your tool specification in LlamaIndex. This lets you restrict your agent to safe tools like `list_locations` while blocking write operations if needed.

Your applicant contact history and resume highlights are never stored on external servers. The LlamaIndex client pulls this data directly through the MCP Server sandbox using your single endpoint token.

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