How to Use the Goldsky (Web3 Data Indexing & Subgraphs) MCP in AutoGen

Q: How do AutoGen agents coordinate to deploy a pipeline using Goldsky (Web3 Data Indexing & Subgraphs)?

One agent drafts the configuration, another runs validatepipeline to check it, and a third calls createpipeline to deploy. They pass JSON payloads between each other, verifying the output at every stage.

Q: What happens if an AutoGen agent calls deletepipeline on the wrong Goldsky (Web3 Data Indexing & Subgraphs) pipeline?

You can configure a supervisor agent to intercept calls to deletepipeline. The supervisor checks the pipeline state via getpipelinestate and requires human approval before executing irreversible actions.

Q: How do AutoGen agents handle indexing rate limits or connection dropouts?

The agents call getpipelinestatus over MCP to monitor connection health. If they detect a dropout, they can use restartpipeline to re-establish the connection or wait for the indexing service to self-heal.

Deploy multi-agent debates to validate, deploy, and monitor Goldsky indexing pipelines with AutoGen.

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Setup Goldsky (Web3 Data Indexing & Subgraphs) with AutoGen

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AutoGen Multi-Agent Deployments

This MCP Server provides the `validate_pipeline` and `create_pipeline` tools for your AutoGen multi-agent workflows. A developer agent drafts a new subgraph YAML, while a QA agent runs `validate_pipeline` to check for configuration errors. Only when both agents agree on the configuration's validity does the deployment agent invoke `create_pipeline`. This consensus-driven approach prevents broken indexing schemas from ever reaching your production database sinks.

Multi-Agent Incident Response

The `get_pipeline_error_count`, `get_pipeline_logs`, and `get_pipeline_status` tools enable collaborative debugging between your AutoGen agents. When an indexing error occurs, a monitoring agent flags the status change and retrieves the error logs. A triage agent analyzes the logs while a DevOps agent decides whether to execute `restart_pipeline` or `pause_pipeline`. They debate the safest recovery path, ensuring you don't corrupt your database with partial block data.

Automated Lifecycle Management

You can manage the entire lifecycle of your Web3 indexing infrastructure using `delete_pipeline`, `resume_pipeline`, and `list_pipelines` inside AutoGen. The agents coordinate to clean up stale pipelines and resume paused ones based on resource budgets. Before running `delete_pipeline`, the agents verify that the target pipeline is indeed inactive by calling `get_pipeline_state`. This multi-step verification loop prevents accidental data loss.

Setup guide

Set up Goldsky (Web3 Data Indexing & Subgraphs) 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 Goldsky (Web3 Data Indexing & Subgraphs) 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="Goldsky (Web3 Data Indexing & Subgraphs)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Goldsky (Web3 Data Indexing & Subgraphs) 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="Goldsky (Web3 Data Indexing & Subgraphs)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Goldsky (Web3 Data Indexing & Subgraphs) 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 Goldsky. 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 Goldsky (Web3 Data Indexing & Subgraphs) MCP in AutoGen

One agent drafts the configuration, another runs `validate_pipeline` to check it, and a third calls `create_pipeline` to deploy. They pass JSON payloads between each other, verifying the output at every stage.

Yes, you can program a security agent to inspect the pipeline configuration before calling `validate_pipeline`. If the agent detects unsafe database credentials or unauthorized sinks, it halts the conversation.

You can configure a supervisor agent to intercept calls to `delete_pipeline`. The supervisor checks the pipeline state via `get_pipeline_state` and requires human approval before executing irreversible actions.

The agents call `get_pipeline_status` over MCP to monitor connection health. If they detect a dropout, they can use `restart_pipeline` to re-establish the connection or wait for the indexing service to self-heal.

This MCP Server runs within a secure, zero-trust V8 sandbox managed by Vinkius. Your API keys and pipeline configurations are processed in memory and never stored, keeping your production database credentials safe from unauthorized agent access.

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