How to Use the Precisely MCP in AutoGen

Q: How do I configure AutoGen to use Precisely tools?

Install autogen-extmcp. Initialize mcpservertools with your HTTP server parameters. The McpToolAdapter automatically converts the JSON schemas so you can pass the tool list straight into your AssistantAgent constructor.

Q: How do agents handle missing property data?

If getpropertyinfo returns empty fields for a rural address, the agent designed to read that data will flag the missing variables. It can then ask a secondary agent to estimate values or prompt the human user for manual input.

Q: What happens to the crime risk coordinates after the debate?

The server evaluates the latitude and longitude through enrichcrimerisk to return normalized threat indices. Because Vinkius hosts this on ephemeral infrastructure, the exact geographic coordinates are purged from memory the millisecond the HTTP response completes.

Equip your AutoGen multi-agent debates with hard location intelligence, property facts, and risk metrics.

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Fuel multi-agent risk negotiations

Using `enrich_flood_risk` gives your risk-assessment agent concrete FEMA zone data to challenge optimistic property valuations. While one agent pushes a high assessment based on `get_property_info` lot sizes, the risk agent counters by citing base flood elevations. They debate the actual viability of the site using hard numbers. This consensus-driven approach prevents single-point failures in automated decision making. A third compliance agent can invoke `get_local_tax` to verify if the overlapping county and city tax rates kill the deal's margin. The AutoGen framework forces them to reconcile the environmental threat with the financial reality before returning a final verdict.

Precisely MCP Server location profiling

Firing `enrich_demographics` arms your market-research agent with household income brackets and spending patterns. Simultaneously, a security-focused agent runs `enrich_crime_risk` to check if the local burglary or assault indices exceed the national average of 100. They cross-examine the neighborhood's economic upside against its physical risks. The debate continues until both agents agree on a composite neighborhood score. Instead of a single LLM trying to weigh conflicting priorities, you have specialized personas arguing their specific domains. The final output is a negotiated summary backed by census-block facts.

Establish ground truth for coordinates

Calling `verify_address` forces all agents to operate on a standardized, deliverable location before the debate even starts. If an agent tries to analyze a fuzzy user input, the mapping agent intercepts and runs `geocode_address` to establish the exact S8 rooftop precision code. If coordinates are provided instead of text, the system uses `reverse_geocode` to establish the street-level context. Another agent might check `get_timezone` to ensure any proposed construction schedules align with local daylight saving rules. Every geographical assumption gets challenged and verified.

Setup guide

Set up Precisely 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 Precisely 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="Precisely_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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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="Precisely_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Precisely. 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 Precisely MCP in AutoGen

Install `autogen-ext[mcp]`. Initialize `mcp_server_tools` with your HTTP server parameters. The `McpToolAdapter` automatically converts the JSON schemas so you can pass the tool list straight into your `AssistantAgent` constructor.

They process sequentially based on the conversation turn. A mapping agent might call `geocode_address` first, post the coordinates to the chat, and then a financial agent reads that message to run the tax lookup.

If `get_property_info` returns empty fields for a rural address, the agent designed to read that data will flag the missing variables. It can then ask a secondary agent to estimate values or prompt the human user for manual input.

No. The MCP standard handles the translation. Your agents automatically understand that `autocomplete_address` requires a partial string and returns an array of 10 ranked candidates because the server provides the exact parameter definitions.

The server evaluates the latitude and longitude through `enrich_crime_risk` to return normalized threat indices. Because Vinkius hosts this on ephemeral infrastructure, the exact geographic coordinates are purged from memory the millisecond the HTTP response completes.

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