How to Use the USAJOBS (OPM) MCP in LlamaIndex

Build knowledge-augmented RAG apps for USAJOBS (OPM) using LlamaIndex.

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Connect USAJOBS (OPM) MCP to LlamaIndex

Create your Vinkius account to connect USAJOBS (OPM) 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 USAJOBS (OPM) with LlamaIndex

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Indexing Job Details with the MCP Server

The `get_announcement_text` tool pulls long text fields from both current and past job postings. When you pass this output to your LlamaIndex setup, it gets indexed into a vector store for semantic search. The result means you can query historical or detailed requirements and get answers grounded in the actual API data, not just general knowledge.

Building Searchable Job Datasets

Use `get_historic_joas` to retrieve bulk job posting data. By indexing this output, your RAG application treats the entire dataset as a single, searchable knowledge base. You can query across thousands of past listings simultaneously. This lets you answer complex questions like, 'What were the typical requirements for Project Manager roles in 2015?'

Filtering and Code Retrieval via LlamaIndex

The `get_code_list` tool provides valid values needed to filter job searches. When LlamaIndex indexes this output, it creates a reference point for your knowledge base. You can query the code list itself to validate inputs. This makes your application extremely reliable because you're using data pulled directly from the USAJOBS (OPM) API structure.

Setup guide

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

LlamaIndex doesn't just read the data; it takes the output from tools like `search_jobs` and indexes it. This means your answers are always directly supported by the retrieved API results.

Yes, that's exactly what this is for. You combine live search data with historical job announcements into one unified, queryable index.

This server touches detailed announcement text, bulk historic records, and searchable code lists. All this data gets indexed to create a deep knowledge base for your LlamaIndex application.

The answers are grounded in the actual API data. If you ask about job requirements, the system points to the specific announcement text retrieved from the MCP Server.

You build an index that combines live search results with historical data. This allows you to compare current job postings against past records easily within the knowledge base.

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