How to Use the Typesense Vector Search MCP in AutoGen

Q: How do I get evidence from past data using Typesense Vector Search and AutoGen?

The agents use searchvectors, passing a text query and vector string. This action pulls highly relevant context that the multiple agents can then debate over to reach a final decision.

Q: Can I inject new facts into a debate using Typesense Vector Search and AutoGen?

Yes. You use indexdocument, supplying the collection name and the JSON object containing the data that needs to be added or updated for agent consideration.

Q: How do I see what collections are available before running a search with Typesense Vector Search and AutoGen?

Running listvectorcollections provides an inventory of all existing collections. This tells you exactly which data sets the agents can reference.

Q: If I delete a document, what impact does this have on the information available to AutoGen?

The deletedocument action removes the record permanently. The multi-agent debate will lose access to that specific piece of evidence immediately.

Drive multi-agent consensus using Typesense Vector Search with AutoGen.

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Setup Typesense Vector Search with AutoGen

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When agents need external facts to debate, they use `search_vectors`. This tool lets them perform a vector similarity search combined with text filtering. The result provides necessary evidence for consensus-driven decision making. Instead of guessing, the multi-agent system gets specific data points it must argue around.

Structure knowledge inputs for AutoGen.

The `create_collection` tool lets you define the precise schema needed. This ensures that when any agent calls `index_document`, the input data is structured and usable by all participating agents. It's how you guarantee consistency across multiple competing viewpoints.

Guarantee clean evidence with AutoGen.

If a piece of information becomes obsolete, don't let an agent argue based on it. You use `delete_document` to permanently remove the record by ID. This cleanup mechanism keeps the data pool relevant for ongoing multi-agent debates.

Setup guide

Set up Typesense Vector Search 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 Typesense Vector Search 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="Typesense Vector Search_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Typesense Vector Search 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="Typesense Vector Search_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Typesense Vector Search 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 Typesense Vector Search. 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 Typesense Vector Search MCP in AutoGen

The agents use `search_vectors`, passing a text query and vector string. This action pulls highly relevant context that the multiple agents can then debate over to reach a final decision.

The server handles structured document records, which include metadata and vectors. The schema dictates exactly what types of information the agents can access.

Yes. You use `index_document`, supplying the collection name and the JSON object containing the data that needs to be added or updated for agent consideration.

Running `list_vector_collections` provides an inventory of all existing collections. This tells you exactly which data sets the agents can reference.

The `delete_document` action removes the record permanently. The multi-agent debate will lose access to that specific piece of evidence immediately.

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