JobScore MCP Server for Pydantic AI 10 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect JobScore through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 JobScore "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in JobScore?"
)
print(result.data)
asyncio.run(main())
* 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 JobScore MCP Server
Empower your AI agents with JobScore's comprehensive applicant tracking system. This MCP server allows you to list and retrieve job postings, track candidates, manage hiring teams and departments, and view hiring sources directly through the JobScore API. Ideal for automating recruitment workflows and talent acquisition.
Pydantic AI validates every JobScore 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.
The JobScore 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 JobScore to Pydantic AI via MCP
Follow these steps to integrate the JobScore 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 JobScore with type-safe schemas
Why Use Pydantic AI with the JobScore MCP Server
Pydantic AI provides unique advantages when paired with JobScore 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 JobScore integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your JobScore connection logic from agent behavior for testable, maintainable code
JobScore + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the JobScore MCP Server delivers measurable value.
Type-safe data pipelines: query JobScore with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple JobScore tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query JobScore and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock JobScore responses and write comprehensive agent tests
JobScore MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect JobScore to Pydantic AI via MCP:
get_candidate
Returns contact history, resume highlights (if available), and current application status. Use this before an interview or when evaluating an applicant. Retrieves details for a specific candidate
get_job
Includes job descriptions, requirements, and hiring team identifiers. Use this to provide detailed information about a specific opening. Retrieves details for a specific job
get_me
Use this to verify connection status and identity. Gets current authenticated user info
list_candidates
Includes candidate names, current stage, and IDs. Essential for monitoring the talent pool and identifying new applications. Lists all candidates
list_departments
g., Engineering, Marketing) used to categorize jobs in JobScore. Useful for filtering hiring data by business unit. Lists all departments
list_hiring_teams
Useful for identifying recruiters and hiring managers associated with specific jobs. Lists all hiring teams
list_jobs
Returns job titles, IDs, and departments. Use this to identify active positions or find a job ID for candidate management. Lists all jobs in JobScore
list_locations
Useful for understanding the geographical scope of hiring efforts. Lists all office locations
list_sources
g., "LinkedIn", "Referral", "Job Board") from which candidates are originating. Essential for analyzing the effectiveness of hiring channels. Lists all candidate sources
list_users
Useful for identifying team members and their roles. Lists all users in the account
Example Prompts for JobScore in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with JobScore immediately.
"List all open jobs in JobScore."
"Show me the details for candidate ID '789'."
"Check the hiring team for the 'Software Engineer' job."
Troubleshooting JobScore MCP Server with Pydantic AI
Common issues when connecting JobScore to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiJobScore + Pydantic AI FAQ
Common questions about integrating JobScore MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
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?
Can I switch LLM providers without changing MCP code?
Connect JobScore with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
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TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect JobScore to Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.