How to Use the Bitquery (Web3 Blockchain GraphQL APIs) MCP in LlamaIndex

Q: How do I index Bitquery (Web3 Blockchain GraphQL APIs) outputs into LlamaIndex?

You call queryv1 or queryv2 to retrieve the target blockchain data as JSON. LlamaIndex then parses this raw output and inserts it directly into your vector store as document nodes.

Q: Can LlamaIndex agents run complex joins using Bitquery (Web3 Blockchain GraphQL APIs)?

Yes, the agent uses queryv2 to execute advanced GraphQL queries that join Transfers, Calls, and DexTrades. The resulting joined dataset is indexed immediately for semantic search.

Q: How does this MCP Server handle authentication for Bitquery (Web3 Blockchain GraphQL APIs) during LlamaIndex indexing jobs?

The agent runs generatetoken to fetch a fresh OAuth2 access token. This token is reused across all subsequent historical or streaming queries in that session.

Q: Can I filter which blockchain tools LlamaIndex has access to?

You can use the allowedtools filter during setup to restrict your agent. For example, you can limit it to queryv1 if you only want historical data indexing.

Index real-time and historical blockchain data directly into LlamaIndex vector stores.

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Connect Bitquery (Web3 Blockchain GraphQL APIs) MCP to LlamaIndex

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Setup Bitquery (Web3 Blockchain GraphQL APIs) with LlamaIndex

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Index real-time streams with this MCP Server

This MCP Server connects LlamaIndex directly to live blockchain events using `query_v2`. The tool retrieves live data like DexTrades and BalanceUpdates, which your agent indexes into your vector database. You query this index later to find patterns in past trades. The agent searches through actual API data instead of guessing or relying on outdated training sets.

Ground your LlamaIndex queries in historical block data

The `query_v1` tool fetches historical data across 40+ chains to ground your agent's responses. Your agent executes precise GraphQL queries and indexes the raw JSON output for semantic search. This prevents hallucinations when asking about old transactions or contract events. You get factual answers backed by verifiable on-chain records.

Secure token generation inside LlamaIndex pipelines

Use `generate_token` to obtain your active BITQUERY_ACCESS_TOKEN before running heavy indexing jobs. This tool automates authentication so your data ingestion pipelines never stall due to expired credentials. LlamaIndex handles this tool call at the start of your ingestion pipeline. Your agent retains the token in memory to run subsequent queries without manual intervention.

Setup guide

Set up Bitquery (Web3 Blockchain GraphQL APIs) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Bitquery (Web3 Blockchain GraphQL APIs) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Bitquery (Web3 Blockchain GraphQL APIs) tools.",
)
response = await agent.run("List recent Bitquery (Web3 Blockchain GraphQL APIs) data")

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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 Bitquery. 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 Bitquery (Web3 Blockchain GraphQL APIs) MCP in LlamaIndex

You call `query_v1` or `query_v2` to retrieve the target blockchain data as JSON. LlamaIndex then parses this raw output and inserts it directly into your vector store as document nodes.

Yes, the agent uses `query_v2` to execute advanced GraphQL queries that join Transfers, Calls, and DexTrades. The resulting joined dataset is indexed immediately for semantic search.

The agent runs `generate_token` to fetch a fresh OAuth2 access token. This token is reused across all subsequent historical or streaming queries in that session.

You can use the allowed_tools filter during setup to restrict your agent. For example, you can limit it to `query_v1` if you only want historical data indexing.

Your GraphQL queries and credentials travel through an ephemeral, zero-trust V8 sandbox. No query payloads or generated API tokens are stored or shared outside your local session.

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