How to Use the ZipRecruiter MCP in LlamaIndex

LlamaIndex: Index ZipRecruiter job results for knowledge-augmented search.

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Connect ZipRecruiter MCP to LlamaIndex

Create your Vinkius account to connect ZipRecruiter 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 ZipRecruiter with LlamaIndex

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Indexing Job Results with LlamaIndex

The MCP allows you to feed the raw output of a job search into a vector store. This means every listing's details, including keywords and salary data, become indexed knowledge. You can then query this index later months from now. It transforms transient API results into permanent, semantic context that your agent uses for answers.

Retrieval Augmented Generation (RAG) with LlamaIndex

Don't rely on memory or vague prompts. Use this MCP to ground your AI client in real data. When you ask, 'What kind of jobs did I look at last month?', the index pulls specific job examples from the stored listings. Your agent gets answers based on actual API calls and structured results, not guesses.

Data Persistence for MCP Server Usage in LlamaIndex

The ability to store search data is huge. Once you run a job query using the tool, the index captures it. You can then build applications that combine documents (like company PDFs) with live job market data. This gives you one unified knowledge source for both internal and external data points.

Setup guide

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

It takes the raw output from job searches—the keywords, locations, and salary details—and converts them into vector embeddings. These are stored in a searchable index.

Yes. If you index the results, you can query that knowledge base months later and get specific details back, like 'Show me all high-paying roles in Seattle from last quarter.'

It's great. You index data over time; the system can then run semantic searches across those indexed periods to reveal trends you might not notice otherwise.

This MCP touches job listing details, specifically keywords, locations, and salary information. All these fields are treated as indexable knowledge.

The filtering happens both at the API level (during the initial search) and later in the RAG stage, where you can semantically filter the indexed results based on natural language queries.

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