Bitquery MCP for AI. Analyze cross-chain data without writing boilerplate code.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
Bitquery (Web3 Blockchain GraphQL APIs) lets your AI client query real-time and deep historical data across major blockchains like Ethereum, Solana, Bitcoin, and Tron.
It connects to over 40 chains using a unified GraphQL interface, letting you analyze DeFi trades, monitor smart contract events, or audit fund movements without needing complex node infrastructure setup.
What your AI can do
Generate token
Uses your API keys to generate a secure OAuth2 token needed for all subsequent blockchain queries.
Query v1
Runs a GraphQL query against the Historical V1 API, ideal for checking past data across many chains.
Query v2
Executes an advanced GraphQL query against the Streaming V2 API, supporting complex joins and live filtering.
Retrieves complex data sets and transaction logs across multiple blockchains for long-term analysis.
Streams real-time events, smart contract calls, and token transfers as they occur on compatible networks.
Tracks liquidity changes, major trades, and overall performance within DeFi protocols using specialized data cubes.
Breaks down complex protocol behavior by analyzing internal calls and emitted events from contracts.
Generates the necessary OAuth2 token using your credentials, ensuring secure and automated connectivity for subsequent queries.
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Bitquery (Web3 Blockchain GraphQL APIs) - 3 Tools
These three tools let you manage authentication credentials, execute deep historical queries, or stream advanced real-time data across the entire blockchain ecosystem.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Bitquery (Web3 Blockchain GraphQL APIs) on VinkiusGenerate Token
Uses your API keys to generate a secure OAuth2 token needed for all subsequent blockchain queries.
Query V1
Runs a GraphQL query against the Historical V1 API, ideal for checking past data...
Query V2
Executes an advanced GraphQL query against the Streaming V2 API, supporting complex...
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Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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Make Your AI Do More
Start with Bitquery (Web3 Blockchain GraphQL APIs), then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
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- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
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.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 3 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Tracking crypto assets used to feel like juggling dozen of dashboards.
Today, tracking a single asset’s journey across the web is painful. You check Etherscan for Ethereum, then switch tabs and use a separate tool for Solana data. If you want to know how much liquidity moved from Chain A to Chain B over six months, you spend hours writing custom scripts, dealing with different date formats, and managing dozens of API keys just to stitch the story together.
With this MCP, that manual process vanishes. You tell your agent what you need—for example, 'Show me all USDC trades between Polygon and Solana last quarter.' The system handles the cross-chain data stitching, pulling historical records via `query_v1` and presenting a single, unified answer.
Accessing Blockchain Data with Bitquery (Web3 Blockchain GraphQL APIs)
You no longer need to write boilerplate code for every chain or every data point. The agent handles the authentication using `generate_token` and routes your request through either `query_v1` or `query_v2`, regardless of whether you're looking at Bitcoin blocks or live smart contract events.
The difference is simple: instead of spending half a day writing integration glue code, you spend ten minutes asking the right question. It’s that straightforward.
What your AI can actually do with this
Need to see what's really happening on the blockchain? This MCP gives your agent direct access to deep Web3 data from dozens of networks through one standard connection. You don't have to worry about juggling multiple APIs or setting up individual nodes for every chain you care about. Whether you need to track a fund's movement years ago, or monitor a smart contract call happening right now, the system handles it all via GraphQL.
For instance, if you're analyzing liquidity pools, you can pull trade data and token performance metrics instantly. It’s like having an entire crypto market dashboard connected directly to your AI agent. When you subscribe through Vinkius, you connect your preferred client once, and suddenly have access to this full suite of blockchain intelligence.
This lets you build complex analytical workflows—from auditing past transactions using query_v1 for historical analysis, to watching live transfers on EVM chains using query_v2. It's all done in natural language conversation.
019e5cff-9aef-72f0-8f37-3a8f6cad5aba Here's how it actually works
The bottom line is you manage access credentials once and then run complex queries using natural language prompts.
First, run generate_token to get an OAuth2 access token by providing your Client ID and Secret.
Next, pass that generated Bearer token as a credential when calling either query_v1 for historical data or query_v2 for real-time streams.
Finally, issue the GraphQL query string directly to the appropriate tool to execute the analysis against the blockchain.
Who is this actually for?
Web3 Developers, data analysts, and security researchers need this. If your job involves tracking digital assets or understanding protocol mechanics across multiple chains, you're already dealing with the pain of fragmented APIs. You shouldn't have to write boilerplate code just to access basic block height information.
Uses this MCP to pull cross-chain metrics, comparing token performance and liquidity changes across different major networks without writing complex SQL joins.
Audits historical transactions using query_v1 to trace fund movements or verifies current protocol behavior by monitoring live events via query_v2.
Integrates this MCP into their agent workflows, letting the AI client perform complex data retrieval and debugging directly from their IDE context.
What Changes When You Connect
See it in action
Tracking a Suspect Fund Movement
A security researcher wants to trace funds moving from an initial exchange through five different protocols. They ask their agent, which uses query_v1, to map the entire journey across multiple chains and identify where the assets paused.
Monitoring a Live Protocol Launch
A product manager needs to see if a new DeFi protocol is attracting liquidity in real-time. They ask their agent to use query_v2 to stream incoming transfers and track the rate of change on Ethereum.
Calculating Market Impact
A quantitative analyst needs to know if a large trade affected token pricing significantly. The agent uses specialized cubes (like those found in query_v2) to calculate price impact based on recent, high-volume trades.
Debugging Smart Contract Failures
A developer runs into an unexpected transaction failure. Instead of manually querying logs, they instruct their agent to use query_v2 to analyze the specific sequence of calls and events that led to the contract error.
The honest tradeoffs
Treating APIs as individual endpoints
Trying to find Bitcoin historical data using a tool designed only for Ethereum, or having to write separate code blocks for every single chain you want to check.
Use this MCP. It provides a unified GraphQL interface that abstracts away the underlying network differences. You just point your query at the desired chain and let the system handle the rest.
Ignoring authentication complexity
Manually handling token expiration, refreshing credentials, or passing different API keys for historical vs. live data.
Always start by running generate_token. This tool handles the secure OAuth2 flow, giving you a single credential that your agent can use reliably across all subsequent queries.
Querying only simple balances
Asking for just 'the current balance' without context. You miss critical details like transaction origin, time stamps, or the specific contract call responsible.
Use query_v2 instead. It supports complex joins and advanced filtering, allowing you to analyze not just the balance, but how it got there (e.g., tracking a transfer event).
When It Fits, When It Doesn't
Use this MCP if your job requires analyzing data across multiple blockchains or if you need both historical context and live streaming capabilities. If your goal is simple—like checking only the current ETH balance on Ethereum Mainnet—you might be overkill, but for anything involving cross-chain analysis or deep DeFi metrics (which require specialized cubes like DexTrades), this is the right choice. Don't use it if you are only interacting with a single, isolated internal database; then, an SQL connector would suffice. But if that data needs to be validated against real-world blockchain activity, start here.
Questions you might have
How do I use Bitquery (Web3 Blockchain GraphQL APIs) for real-time data? +
Use query_v2 to access live streaming capabilities. This tool supports advanced joins and filtering, letting you monitor transfers or contract calls as they happen on compatible networks.
What is the purpose of generate_token in this MCP? +
You must run generate_token first. It uses your Client ID and Secret to create a secure OAuth2 token, which serves as the required credential for all subsequent queries.
Can I use query_v1 or query_v2 for historical analysis? +
query_v1 is designed specifically for executing GraphQL queries against the Historical V1 API, making it perfect for auditing and looking at past data sets.
Does this MCP support all major chains like Ethereum and Solana? +
Yes. It covers 40+ chains, including Bitcoin, Tron, Polygon, BNB, Ethereum, and Solana, making it a true cross-chain tool.
How do I use `generate_token` to manage my long-term access credentials securely? +
You should treat the token like a password and generate new tokens periodically. The system uses OAuth2, so refreshing your credential using generate_token prevents stale connections and maintains secure, automated access for your agent.
When querying DeFi data, which tool do I use to track trades and token performance? Is it `query_v1` or `query_v2`? +
query_v2 is the correct choice because it supports specialized cubes. You'll want to use the DexTrades or DexTradesByTokens cubes within your GraphQL query to track liquidity and token performance across chains.
If I need deep insight into smart contract interactions, how should I structure my request using `query_v2`? +
You access protocol mechanics by targeting the specific data types. Use query_v2 to query the Calls, Events, and Instructions cubes. This lets you see exactly how contracts interact and what internal transactions occurred.
When using `query_v2` for cross-chain analysis, can I limit my results to specific chains or networks? +
Yes, the GraphQL structure allows it. You don't need separate calls for every chain; instead, you include filters specifying the desired network names or IDs directly within your query string when calling query_v2.
What is the difference between query_v1 and query_v2? +
Use query_v1 for historical data across 40+ legacy and non-EVM chains (like Bitcoin). Use query_v2 for real-time streaming and advanced filtering on EVM chains and Solana, supporting complex joins like 'joinCalls'.
How do I obtain an access token if I only have a Client ID and Secret? +
You can use the generate_token tool. Provide your Bitquery Client ID and Secret, and the tool will return a Bearer token that you can use as your BITQUERY_ACCESS_TOKEN credential.
Can I query data from multiple regions? +
Yes. Both query_v1 and query_v2 support an optional region parameter. You can choose between 'global', 'asia', or 'us' to optimize latency based on your location.
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