How to Use the Workable MCP in LlamaIndex

Q: Can LlamaIndex search historical Workable job openings?

Yes, once you run listjobs and index the results, your agent can perform semantic searches on those structured job listings. You query the knowledge base, not just a live endpoint.

Q: How does LlamaIndex handle candidate data from Workable?

The tool output—like the result of getcandidateprofile—is ingested into the vector store. This allows you to query by semantic meaning (e.g., 'creative,' 'leadership') instead of just exact keywords.

Query past hiring decisions and job data using LlamaIndex.

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Connect Workable MCP to LlamaIndex

Create your Vinkius account to connect Workable 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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Indexing Job Openings with MCP Server

You can start by calling `list_jobs` to get a list of active openings. When you pass this structured output to your index, the job titles and IDs become searchable knowledge. You're not just retrieving data; you're adding it permanently to the vector store. Later on, you query that index—say, 'What were our primary engineering roles last quarter?'—and LlamaIndex grounds the answer in the actual list of jobs provided by the MCP Server.

Creating a Candidate Knowledge Base with MCP Server

When you use `get_candidate_profile`, the output details for that candidate are indexed. This means you can later ask, 'Which candidates have experience in X?' and LlamaIndex searches across all stored profiles to find matches. This makes your agent's knowledge durable. The data isn't just a one-time API call; it becomes part of the searchable corpus for RAG applications.

Tracking Account Members with LlamaIndex

Run `list_account_members` to pull current team details. Indexing this list means you can query specific roles or departments months later, even if the original API call was part of a different session. This capability turns transient data into long-term knowledge. You build an index that answers questions like: 'Who were our primary HR contacts in Q3?'

Setup guide

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

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Common questions about Workable MCP in LlamaIndex

Yes, once you run `list_jobs` and index the results, your agent can perform semantic searches on those structured job listings. You query the knowledge base, not just a live endpoint.

The tool output—like the result of `get_candidate_profile`—is ingested into the vector store. This allows you to query by semantic meaning (e.g., 'creative,' 'leadership') instead of just exact keywords.

The server provides job details, candidate profiles, and team member lists. All these structured outputs become the source material for your searchable knowledge index.

Yes. The client is designed to handle tool specifications and include resources (`include_resources=True`), making sure that live API data from the Workable MCP Server gets correctly added to your index.

You combine multiple tool outputs (like job lists and account member lists) into a single, unified knowledge base. Your agent queries the combined index, getting answers grounded in all that API data.

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