How to Use the Jobtoolz MCP in LlamaIndex

Q: Can LlamaIndex search Jobtoolz candidates semantically?

Yes, you can fetch candidate details using getcandidate and load them into a vector index. This allows you to run semantic search queries to find candidates with specific backgrounds or skills.

Q: How do I filter Jobtoolz roles by location in LlamaIndex?

Your agent can call listlocations to identify valid office regions. It then uses those parameters to filter the jobs returned by listjobs before indexing them.

Index your Jobtoolz candidate profiles and job requirements into LlamaIndex for semantic search.

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Connect Jobtoolz MCP to LlamaIndex

Create your Vinkius account to connect Jobtoolz 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 Jobtoolz with LlamaIndex

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Index Recruiting Data for Semantic Search

Turn your recruitment database into a queryable knowledge base by fetching candidate details with `get_candidate`. LlamaIndex can retrieve these candidate details and index the text directly into your vector store. You can then search across all applications using natural language queries. This eliminates the need to write complex SQL queries or filter scripts. The agent reads the live output from `list_candidates` and builds an index that stays current with your active hiring pipeline.

Ground Agent Responses in Real Job Details

Avoid hallucinations when discussing open roles by using `get_job` to pull actual descriptions and requirements. It ensures every response is grounded in real data. This setup prevents your agent from making up requirements or pipeline stages. The framework combines live API data from `list_jobs` with your internal documents using an MCP connection. This creates a unified query engine where you can ask which candidates match a specific job description.

Map Hiring Stages and Sources with LlamaIndex

Track where your best applicants come from by querying `list_sources` to analyze candidate origins. Your agent can query this alongside `list_stages` to map candidate progression. It structures this data into your index for easy reporting. Instead of looking at raw JSON, you can query your index to find out which sources yield the fastest-moving candidates. The tool configuration lets you filter by `list_tags` to focus on specific skill sets.

Setup guide

Set up Jobtoolz 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 Jobtoolz 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 Jobtoolz tools.",
)
response = await agent.run("List recent Jobtoolz 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 Jobtoolz. 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 Jobtoolz MCP in LlamaIndex

Use the basic client to load the tools and convert them using the tool spec helper. Once converted, you can pass them to your agent or use them to load data into your document indexers.

Yes, you can fetch candidate details using `get_candidate` and load them into a vector index. This allows you to run semantic search queries to find candidates with specific backgrounds or skills.

Yes, the server exposes standard tools that integrate directly into your custom workflows. You can build event-driven pipelines that trigger when new candidates are found via `list_candidates`.

Your agent can call `list_locations` to identify valid office regions. It then uses those parameters to filter the jobs returned by `list_jobs` before indexing them.

Your applicant records and hiring pipelines are protected by ephemeral execution environments. This MCP setup secures the credentials on a need-to-know basis, ensuring that data retrieved from the API is only processed within your isolated session.

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