How to Use the Troops MCP in LlamaIndex

Q: How can I query past candidate records using Troops and LlamaIndex?

LlamaIndex indexes getcandidate outputs. You stop relying on simple IDs; you start asking questions like, 'What was the salary range for candidates who applied last quarter?' The answer is grounded in stored data.

Q: Does LlamaIndex help manage job offers with Troops?

Yes. By indexing listjobs results, you can build RAG applications that query the context of your open roles—not just their names or statuses. You get deep insights into hiring trends.

Q: What kind of HR history tracking does Troops and LlamaIndex support?

You track everything: from initial job creation (createjob) to the final timesheet submission (submittimesheet). Each API call updates your searchable index, building a comprehensive organizational memory.

Q: Can I verify contract details using LlamaIndex and Troops?

You can. By indexing the results of generatecontract and signcontract, you allow agents to query specific clauses or signing dates from years prior, making compliance checks simple.

Ground HR decisions in historical data using the Troops MCP Server with LlamaIndex for semantic search.

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Connect Troops MCP to LlamaIndex

Create your Vinkius account to connect Troops 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 Troops with LlamaIndex

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Search deep candidate history via the Troops MCP Server.

The `get_candidate` tool retrieves specific personnel details, which LlamaIndex then indexes into your vector store. You can later query past sessions and ask questions grounded in actual candidate records. Instead of just getting a JSON payload, you get searchable knowledge. If you need to know why Candidate X was flagged six months ago, the index finds that answer using the API data.

Index job listings and status changes with LlamaIndex.

`list_jobs` provides a list of all current job offers. When you run this tool, we ingest those results into your knowledge base. You can then ask semantic questions like 'What open roles are related to backend development?' This means the live status of every job offer becomes part of your searchable documentation.

Build a contract library with LlamaIndex and the MCP Server.

When you run `generate_contract` or subsequently call `sign_contract`, the resulting document metadata is indexed. This means every signed contract becomes part of your knowledge base for compliance checks. You don't just execute the action; you record it as searchable, verifiable history.

Setup guide

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

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Common questions about Troops MCP in LlamaIndex

LlamaIndex indexes `get_candidate` outputs. You stop relying on simple IDs; you start asking questions like, 'What was the salary range for candidates who applied last quarter?' The answer is grounded in stored data.

Yes. By indexing `list_jobs` results, you can build RAG applications that query the *context* of your open roles—not just their names or statuses. You get deep insights into hiring trends.

You track everything: from initial job creation (`create_job`) to the final timesheet submission (`submit_timesheet`). Each API call updates your searchable index, building a comprehensive organizational memory.

You can. By indexing the results of `generate_contract` and `sign_contract`, you allow agents to query specific clauses or signing dates from years prior, making compliance checks simple.

The Troops MCP Server handles sensitive employment records, specifically candidate personal details and timesheet submissions.

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