How do I move a candidate to a specific stage?

Use the `update_opportunity_stage` tool with the unique opportunity ID and the target stage ID. You can find stage IDs using the `list_hiring_stages` tool.

Can I archive an application via the agent?

Yes, use the `archive_hiring_opportunity` tool. You will need to provide the opportunity ID and a valid archive reason ID.

Is it possible to see a candidate's contact information?

Absolutely. Use the `get_candidate_profile` tool with the candidate's unique ID to retrieve their full profile, including email, phone, and links.

Lever MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Lever
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Lever through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Lever "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Lever?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Lever MCP Server

Connect your Lever account to any AI agent to streamline your recruitment and talent acquisition workflows. This MCP server enables your agent to interact with job postings, manage candidate opportunities, and move applications through your hiring pipeline directly.

Pydantic AI validates every Lever tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Posting Oversight — List and retrieve detailed configurations for all your active job advertisements
Opportunity Management — Manage candidate applications (Opportunities), track their status, and move them through hiring stages
Candidate Insight — Access complete candidate profiles, contact details, and interaction histories
Pipeline Control — List hiring stages and automate the archiving of applications with specific reasons
Workflow Automation — Create new job postings or candidate records directly from natural language interfaces

The Lever MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Lever to Pydantic AI via MCP

Follow these steps to integrate the Lever MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Lever with type-safe schemas

Why Use Pydantic AI with the Lever MCP Server

Pydantic AI provides unique advantages when paired with Lever through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Lever integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Lever connection logic from agent behavior for testable, maintainable code

Lever + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Lever MCP Server delivers measurable value.

Type-safe data pipelines: query Lever with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Lever tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Lever and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Lever responses and write comprehensive agent tests

Lever MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Lever to Pydantic AI via MCP:

archive_hiring_opportunity

Archive a candidate opportunity

create_hiring_opportunity

Requires a JSON body with opportunity details. Create a new candidate opportunity

create_job_posting

Requires a JSON body with posting details. Create a new job posting

get_candidate_profile

Get details for a specific candidate (person)

get_opportunity_details

Get details for a specific candidate opportunity

get_posting_details

Get details for a specific job posting

list_hiring_opportunities

List all candidate opportunities (applications)

list_hiring_stages

g., Screen, Interview) configured in your Lever account. List all defined hiring pipeline stages

list_job_postings

List all job postings

update_opportunity_stage

g., move to "Interview" or "Offer"). Move a candidate to a different hiring stage

Example Prompts for Lever in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Lever immediately.

"List all my current job postings in Lever."

"Move opportunity ID 'opp-123' to the 'Interview' stage (ID: 'stage-abc')."

"Get the full profile for candidate ID 'cand-987'."

Troubleshooting Lever MCP Server with Pydantic AI

Common issues when connecting Lever to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Lever + Pydantic AI FAQ

Common questions about integrating Lever MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Lever MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.