How to Use the Firefish MCP in Pydantic AI

Type-safe recruitment automation with Pydantic AI and Firefish.

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Connect Firefish MCP to Pydantic AI

Create your Vinkius account to connect Firefish 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 Firefish with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Strict type validation for Pydantic AI

Ensure your agent never acts on corrupted data. Every response from `get_candidate` is checked against Pydantic models at runtime. If the data structure doesn't match, the agent fails immediately. This prevents the silent errors that plague standard integration setups.

Reliable action execution in Firefish

Use `list_actions` to see exactly what is happening in the CRM. The strongly-typed response gives you full visibility into the history of any record. Your agent can reliably chain these actions together. You get a predictable workflow that doesn't hallucinate missing fields.

Unified MCP Server access for Pydantic AI

Simplify your codebase by using the unified toolset approach. Connect your agent to the Firefish environment once and expose all 12 tools instantly. This removes the complexity of managing multiple connections. You get a clean, typed interface to all your recruitment data.

Setup guide

Set up Firefish 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": {
        "firefish-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Firefish MCP in Pydantic AI

It provides runtime validation that ensures your agent only processes valid data. It stops you from building brittle systems that break on bad API responses.

By forcing every tool response into a predefined schema. If Firefish returns an unexpected field, the agent catches it before it causes a downstream error.

Yes, you can define custom schemas for complex data structures. Your agent will validate the output of `list_placements` against these models.

The integration uses ephemeral tokens for all requests. Your recruitment data is never stored in the agent's memory or long-term storage.

It's straightforward. You initialize the toolset with your endpoint URL, and the agent handles the rest through the standard protocol.

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