How to Use the Ashby MCP in LlamaIndex

Q: How do I configure LlamaIndex to read Ashby data via MCP?

Run pip install llama-index-tools-mcp. Setup a BasicMCPClient with your Vinkius URL, wrap it in an McpToolSpec, and await the async tool list generation.

Q: Can the framework filter available MCP tools?

You restrict access using the allowedtools parameter. This ensures your RAG app only calls read-only endpoints and never accidentally triggers candidate creation.

Index your Ashby recruiting data into searchable LlamaIndex knowledge bases.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

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.

GDPR Free for Subscribers

Setup Ashby with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Vectorize Ashby MCP Server records

LlamaIndex pulls your entire active pipeline using `list_ashby_candidates` and `list_ashby_jobs`. The MCP framework embeds these records into your vector store. Your HR team queries the index using natural language instead of clicking through dashboards. Semantic search finds passive candidates based on past experience. When you run `get_ashby_candidate`, the output becomes permanent context. The AI grounds its answers in actual ATS data.

Query historical interview feedback

Building a RAG application over your hiring decisions requires the `list_ashby_feedback` tool. The system ingests scorecard text and interviewer notes. Users ask why a specific profile was rejected last year. Retrieving specific context happens fast. Calling `get_ashby_application` supplies the exact timeline of the candidate's journey. Your application synthesizes this raw API response with internal hiring guidelines.

Track hiring manager activity

Syncing internal directory data with recruiting metrics starts by calling `list_ashby_users`. The index correlates your team members with their scheduled blocks from `list_ashby_interviews`. You build tools that answer questions about bottlenecks. The system cross-references open roles against who is actually doing the interviewing. It identifies overloaded engineers before they burn out.

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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Ashby now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Ashby MCP in LlamaIndex

Run `pip install llama-index-tools-mcp`. Setup a `BasicMCPClient` with your Vinkius URL, wrap it in an `McpToolSpec`, and await the async tool list generation.

You restrict access using the `allowed_tools` parameter. This ensures your RAG app only calls read-only endpoints and never accidentally triggers candidate creation.

It depends on your indexing strategy. You schedule periodic runs to update the vector store with fresh pipeline movements.

No. RAG architectures force the LLM to cite the exact output from the API. If the data is missing, the query fails gracefully.

The integration handles sensitive PII like phone numbers, emails, and salary expectations. Vinkius routes the traffic through an ephemeral environment where tokens are destroyed immediately after the response resolves.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript