How to Use the Ashby MCP in LlamaIndex

Q: How do I verify the server works before indexing?

Run getaccountcheck. This tool pings the API and confirms your authentication token is valid before you start pulling massive batches of candidate records.

Index your Ashby recruiting data with LlamaIndex. Turn job listings and candidate profiles into a searchable knowledge base.

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Connect Ashby MCP to LlamaIndex

Create your Vinkius account to connect Ashby 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 Ashby with LlamaIndex

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Claude

Perplexity

Build a searchable hiring index

Flat ATS data is hard to query naturally. LlamaIndex fixes this by pulling active records and embedding them into a vector store. Your internal teams can just ask plain English questions about the hiring pipeline. The system uses `list_candidates` and `list_jobs` to ingest your entire current database. From there, McpToolSpec converts the outputs into indexable nodes, letting your RAG setup ground its answers in real-time recruiting facts.

Query this MCP Server for context

Sometimes a vector search needs fresh context before returning an answer. This integration lets your query engine reach out to the live ATS mid-retrieval. Hallucinations drop to zero when the agent checks the source. If a user asks about a specific applicant, the engine triggers `search_candidates` by email. It then runs `get_candidate_info` to fetch the absolute latest resume details, merging that live payload with your indexed historical data.

Ground interview prep in facts

Hiring managers need quick briefs before jumping on a call. You can wire an agent to generate these summaries automatically based on system records. No more digging through email threads for context. The agent calls `list_interviews` to find today's schedule. It follows up with `get_application_info` to pull the specific role requirements, feeding all that structured text into your LlamaIndex FunctionAgent for synthesis.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Ashby. 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 Ashby MCP in LlamaIndex

Install llama-index-tools-mcp and instantiate a BasicMCPClient. Wrap it in McpToolSpec and pass those tools into your ingestion pipeline to start embedding.

The FunctionAgent decides when to hit the live API versus the vector store. It can fire get_job_info to pull the exact current status of a role.

Your RAG application falls back to the previously indexed vector data. Once the connection restores, the agent resumes calling tools like list_applications for fresh updates.

Run get_account_check. This tool pings the API and confirms your authentication token is valid before you start pulling massive batches of candidate records.

The Vinkius endpoint acts as a zero-trust pass-through. Interview details and candidate histories only exist in memory during the request, meaning your sensitive HR data never rests on our infrastructure.

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