How to Use the Workable Alternative MCP in Pydantic AI

Q: How do I list jobs using Workable Alternative with Pydantic AI?

You use listjobs through the MCPToolset. The key benefit is that even though you are listing, the output fields (like job title and ID) will be validated against your Pydantic schema at runtime. This ensures correctness.

Q: What if I need to get candidate details using Workable Alternative with Pydantic AI?

You call getcandidate. If the data coming back is missing a required field, or if the type of 'experience' suddenly changes from a string to an integer, the agent throws an error. It never silently corrupts your workflow.

Manage recruiting records with guaranteed type safety using Pydantic AI.

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Connect Workable Alternative MCP to Pydantic AI

Create your Vinkius account to connect Workable Alternative to Pydantic AI 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 Workable Alternative with Pydantic AI

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Creating Candidates with Type Safety

When you run `create_candidate`, your agent is forced to validate the output against a Pydantic model. If the underlying API sends unexpected data, the agent fails loud—you won't get silent corruption. This guarantees that when you add a new candidate record, all fields are exactly what you expect them to be.

Retrieving Job Details Without Errors

Use `get_job` whenever your agent needs job specifics. Because of Pydantic validation, if the API returns a malformed job ID or description, the agent stops and tells you immediately. This focus on correctness means that whether you're using it with OpenAI, Anthropic, or Gemini models, the data structure is always trustworthy.

Listing Candidates Reliably

Calling `list_candidates` provides a list of records where every field—like names and IDs—is guaranteed to match your defined Pydantic schema. This eliminates guesswork in multi-step processes. It’s the unified approach: you pass the toolset once, and it enforces strict typing across all client models.

Setup guide

Set up Workable Alternative MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "workable-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Workable Alternative tools.",
)

result = await agent.run("List recent Workable Alternative transactions")
print(result.output)

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Common questions about Workable Alternative MCP in Pydantic AI

You use `list_jobs` through the MCPToolset. The key benefit is that even though you are listing, the output fields (like job title and ID) will be validated against your Pydantic schema at runtime. This ensures correctness.

You call `get_candidate`. If the data coming back is missing a required field, or if the type of 'experience' suddenly changes from a string to an integer, the agent throws an error. It never silently corrupts your workflow.

Yep. Since it's model-agnostic, you can pass the toolset to agents running OpenAI, Anthropic, or Gemini. The type validation happens on the response side, making it universally reliable.

For correctness, yeah. Other systems might proceed even if data is weirdly structured; this one fails loud with a validation error. You get guaranteed types every time.

The MCP Server touches Candidate and Job details. The framework ensures that when your agent reads or writes these records, the structural integrity of names, IDs, and stages is maintained.

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