How to Use the Firefish MCP in AutoGen

Q: How do I register the Firefish MCP Server in AutoGen?

Install the extension with pip install -U "autogen-extmcp". Use StreamableHttpServerParams to connect to your Vinkius endpoint, and pass the generated tools list to your AssistantAgent constructor.

Q: Can AutoGen agents collaborate on Firefish candidate profiles?

Yes, one agent can fetch a profile using getcandidate and share the details with another agent in the group chat. They can then debate the candidate's qualifications before deciding to call createcandidate for a new role.

Q: How does AutoGen handle schema conversion for the Firefish MCP Server?

The McpToolAdapter in the AutoGen extension automatically translates the server's tool schemas into the format required by your agents. You don't have to write any manual mapping code.

Q: How does AutoGen protect sensitive Firefish recruitment data?

All agent interactions with tools like listplacements and getcontact are executed within an ephemeral, zero-trust V8 sandbox. Your candidate records and client contact details are never stored or exposed to external networks.

Let specialized AutoGen agents debate candidate matches and job placements using this Firefish MCP Server.

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Connect Firefish MCP to AutoGen

Create your Vinkius account to connect Firefish to AutoGen 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 AutoGen

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Run consensus-driven candidate screening in AutoGen

Manual screening is slow and prone to bias. By exposing `list_candidates` to your AutoGen team, you can have a sourcing agent find profiles while a compliance agent reviews them for role alignment. They debate the candidate's suitability using data fetched via `get_candidate` before presenting a final shortlist. This multi-agent debate ensures a higher standard of screening. Instead of a single LLM making a snap judgment, the agents challenge each other's reasoning using concrete data from your CRM.

Automate client and candidate communications safely

Sending automated messages to clients can backfire if the context is wrong. In AutoGen, you can set up a writer agent to draft outreach and a reviewer agent to verify details using `get_contact` and `get_company`. They work together to ensure the outreach is accurate before any communication is finalized. This cooperative workflow prevents embarrassing mistakes. The reviewer agent checks the draft against active records from `list_actions` to make sure you aren't emailing a client who was contacted earlier today.

Coordinate complex hiring workflows across agents

Managing a placement involves multiple moving parts. Your AutoGen supervisor agent can assign tasks to sub-agents, like tracking active job adverts with `list_adverts` or auditing final placements with `list_placements`. This keeps your entire recruitment pipeline organized without manual oversight. The MCP Server acts as the single source of truth for all agents in the group. They share tool outputs, ensuring that the agent managing jobs with `list_jobs` is always aligned with the agent tracking candidates.

Setup guide

Set up Firefish MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Firefish tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Firefish_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Firefish data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Firefish_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Firefish data")
    print(result.messages[-1].content)

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 Firefish. 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.

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Common questions about Firefish MCP in AutoGen

Install the extension with `pip install -U "autogen-ext[mcp]"`. Use `StreamableHttpServerParams` to connect to your Vinkius endpoint, and pass the generated tools list to your `AssistantAgent` constructor.

Yes, one agent can fetch a profile using `get_candidate` and share the details with another agent in the group chat. They can then debate the candidate's qualifications before deciding to call `create_candidate` for a new role.

The `McpToolAdapter` in the AutoGen extension automatically translates the server's tool schemas into the format required by your agents. You don't have to write any manual mapping code.

This integration supports both stdio and Streamable HTTP transports. When hosted on Vinkius, you will use the secure HTTP transport with your single endpoint token.

All agent interactions with tools like `list_placements` and `get_contact` are executed within an ephemeral, zero-trust V8 sandbox. Your candidate records and client contact details are never stored or exposed to external networks.

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