How to Use the Freshteam MCP in LlamaIndex

Q: How do I set up Freshteam tools in LlamaIndex?

Install llama-index-tools-mcp via pip. Initialize a BasicMCPClient, wrap it with McpToolSpec, and call totoollistasync() to pass the array into your FunctionAgent.

Index your Freshteam HR data into searchable vector stores using LlamaIndex and semantic search.

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Connect Freshteam MCP to LlamaIndex

Create your Vinkius account to connect Freshteam 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 Freshteam with LlamaIndex

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Indexing HR data via MCP Server

This MCP Server allows your LlamaIndex application to pull live HR records and embed them directly into your knowledge base. You run `list_job_postings` and `get_job_posting_details` to build an instant, queryable index of every open requisition. The days of hallucinating job requirements are over. When a user asks about engineering roles, your agent searches the vector store, retrieving actual descriptions and salary bands grounded in real API responses.

Candidate tracking and RAG pipelines

Your MCP Server feeds `list_job_applicants` and `get_applicant_details` into your ingestion pipeline to create a semantic map of your talent pool. Your RAG app can now answer complex queries about candidate experience levels without manual sorting. Combining this with document loaders means you can cross-reference internal rubrics against live applicant data. The agent knows exactly who applied and what skills they bring because the data lives in the index.

Mapping organizational structures

Calling `list_departments`, `get_employee_details`, and `list_office_branches` through the MCP protocol gives LlamaIndex the raw material to map your entire company structure. It embeds these relationships so users can ask natural language questions about reporting lines. You keep the index fresh by scheduling periodic updates. The system runs `check_hris_status` to ensure connectivity, then pulls the latest headcount numbers to replace outdated nodes in your vector store.

Setup guide

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

Install `llama-index-tools-mcp` via pip. Initialize a `BasicMCPClient`, wrap it with `McpToolSpec`, and call `to_tool_list_async()` to pass the array into your FunctionAgent.

You can restrict access using the allowed tools filter. If you only want an agent reading data, you simply exclude the employee creation endpoint during setup.

Yes. By setting include resources to true, your agent treats the employee and applicant endpoints as direct data sources for the vector index rather than just callable functions.

Your ingestion script needs to handle the tool outputs iteratively. The agent calls the list tools, parses the returned arrays, and feeds those chunks into your embedding model.

Vinkius operates on a zero-trust architecture. When LlamaIndex retrieves sick leave or vacation balances, the MCP connection authenticates ephemerally. You control the vector store where that sensitive information ultimately lives.

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