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

Q: How do I set up the Goldsky (Web3 Data Indexing & Subgraphs) MCP Server inside Pydantic AI?

Install pydantic-ai-slimmcp and configure the MCPToolset with your Vinkius HTTP endpoint. Pass the toolset into your Agent constructor to give your model access to all 12 indexing tools.

Q: How does Pydantic AI handle errors returned by Goldsky (Web3 Data Indexing & Subgraphs)?

When tools like getpipelineerrorcount return data, Pydantic AI validates the response payload against its internal models. If the schema mismatches, it raises a validation error immediately.

Q: What is the benefit of using getpipelinestate over standard status checks?

The getpipelinestate tool returns the internal indexing position and block progress. This provides deeper telemetry than a simple running or paused status, allowing precise synchronization.

Run type-safe Web3 indexing operations using Pydantic AI and this MCP Server.

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Setup Goldsky (Web3 Data Indexing & Subgraphs) with Pydantic AI

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Validate Web3 pipelines with Pydantic AI

The `validate_pipeline` tool verifies your deployment configuration files before they are sent to the indexing engine. Pydantic AI ensures that the returned validation schemas match your strict Python models at runtime. If the configuration is correct, the agent triggers `create_pipeline`. This MCP server ensures that any unexpected API response triggers a loud validation error.

Track indexer state with model-level validation

The `get_pipeline_status` tool fetches the live state of your subgraphs, converting the raw API response into strongly typed Python objects. Your agent uses this structured data to verify indexing progress. To inspect failures, the agent calls `get_pipeline_error_count` and `get_pipeline_logs`. Because every field is validated against Pydantic schemas, your agent never operates on corrupted or incomplete log data.

Manage pipeline lifecycles without manual scripts

The `pause_pipeline` tool allows your agent to suspend data ingestion when downstream databases are undergoing migrations. This prevents data loss and maintains strict consistency across your stack. Once the database is ready, the agent runs `resume_pipeline` or `restart_pipeline` to catch up with the blockchain. This programmatically coordinates your indexing state with your application backend.

Setup guide

Set up Goldsky (Web3 Data Indexing & Subgraphs) 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": {
        "goldsky-web3-data-indexing-subgraphs-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Goldsky (Web3 Data Indexing & Subgraphs) tools.",
)

result = await agent.run("List recent Goldsky (Web3 Data Indexing & Subgraphs) transactions")
print(result.output)

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Common questions about Goldsky (Web3 Data Indexing & Subgraphs) MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]` and configure the `MCPToolset` with your Vinkius HTTP endpoint. Pass the toolset into your `Agent` constructor to give your model access to all 12 indexing tools.

When tools like `get_pipeline_error_count` return data, Pydantic AI validates the response payload against its internal models. If the schema mismatches, it raises a validation error immediately.

Yes, the MCP integration connects directly with Pydantic AI's asynchronous runtime. This allows your agent to run tools like `list_pipelines` and `get_pipeline_status` concurrently without blocking.

The `get_pipeline_state` tool returns the internal indexing position and block progress. This provides deeper telemetry than a simple running or paused status, allowing precise synchronization.

Your API tokens and pipeline configurations are processed inside a zero-trust MCP Server sandbox. Raw indexing schemas are never cached or exposed to external networks, maintaining strict data isolation.

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