Bitquery

@Bitquery show me the top 10 Ethereum addresses by transaction count in the last 24 hours

This example would query Bitquery's realtime database for Ethereum mainnet transactions, grouping by sender address and using the count metric to aggregate transaction volume. The response would return a ranked list showing addresses like '0x1234...abcd' with counts ranging from thousands to tens of thousands of transactions, depending on current network activity. The data reflects the most active wallets, exchanges, or smart contracts during the specified timeframe.

This demonstrates Bitquery's read-heavy analytics capabilities using the realtime database for recent blockchain data. Requires an API key with query permissions. Note that 'last 24 hours' queries hit the realtime database, which may have rate limits on complex aggregations — consider narrowing the time window if you encounter throttling.

@Bitquery query the archive database for all USDC transfers over $1M on Polygon between January 1-7, 2024

This example would use the archive database tool to retrieve historical ERC-20 transfer events for the USDC contract on Polygon, filtering by transfer amount and date range. The response would include transaction hashes, sender/receiver addresses, exact transfer amounts in USDC, block numbers, and timestamps. Expect results delayed by tens of minutes to hours compared to realtime data, as the archive database prioritizes completeness over immediacy.

This showcases Bitquery's historical data access for compliance or research use cases. The archive database is essential for queries beyond the realtime retention window. Be aware that archive queries can be slower and more expensive (in API credits) than realtime queries — the delay is a trade-off for accessing the full blockchain history.

@Bitquery compare daily unique active addresses across Ethereum, Polygon, and Arbitrum for the past week, then summarize which chain had the most growth

This example would use the combined database and count distinct metric to calculate unique addresses interacting with each chain daily, then leverage aliases to structure the multi-chain query clearly. The raw response would show seven days of unique address counts per chain — for instance, Ethereum might show 400K-500K daily, Polygon 200K-300K, Arbitrum 150K-250K. The AI would then analyze the trend lines to identify that, hypothetically, Arbitrum grew 18% week-over-week while Ethereum remained flat, making Arbitrum the growth leader.

This demonstrates Bitquery's synthesis power: pulling multi-chain metrics in one query, then pairing the data with AI reasoning to answer a strategic question. The combined database ensures you get both historical baseline and latest data. Watch for query complexity limits — comparing three chains over seven days is manageable, but scaling to dozens of chains or months may require batching requests.

When real-time blockchain data beats manual Discord scraping

A 6-person crypto fund tracks new token launches across Ethereum and BSC to spot early liquidity opportunities. They used to monitor Telegram channels and Dexscreener manually—slow, error-prone, and they'd miss launches overnight. Bitquery's Combined Database Query tool lets them pull both historical launch patterns and live mint events in one call, feeding a Slack channel that alerts the team within seconds. The Count Distinct Metric tool tracks unique deployer addresses to filter out spam contracts. This MCP is the right call if your team needs sub-minute latency on multi-chain events and can write basic GraphQL. If you're only tracking one chain or checking prices once a day, a simpler REST API or Dune dashboard is cheaper. For funds making time-sensitive calls on new assets, Bitquery's 18-tool suite turns blockchain noise into actionable signals without hiring a data engineer.

How a 3-person dev team built compliance reporting in a sprint

A DeFi protocol with 3 backend engineers needed to generate monthly compliance reports showing transaction volumes, unique users, and gas spent across their smart contracts. They were manually exporting CSVs from Etherscan and pivoting in Google Sheets—taking 2 days per report. The Archive Database Query tool gave them full historical access to their contract events, and the Count Metric and Count Distinct Metric tools let them aggregate totals and unique wallet counts in a single GraphQL call. They built a Python script that runs monthly, pulls the data via Bitquery's MCP, and outputs a PDF for their legal team. The trade-off: archive data lags by 30-60 minutes, so this doesn't work for live dashboards. If your compliance window is monthly or quarterly and you need auditable historical data without managing your own node, Bitquery's archive tooling is the fastest path from zero to automated reporting.

When cross-chain queries justify the API key overhead

A 5-person product team at an NFT aggregator wanted to compare minting trends across Ethereum, Polygon, and Solana to decide which chain to prioritize for their next feature. They needed 90 days of mint counts, unique minters, and average gas fees per chain. The Early Access Program Query tool gave them streaming access to Solana (not in the main v2 endpoint), and the Conditional Metrics Snippet tool let them filter out test mints and bot activity in the same query. They ran the analysis in an afternoon instead of stitching together three separate APIs. The catch: if you're only analyzing one chain or your data needs are static (like a one-time research report), paying for Bitquery's API key is overkill—use a free Dune query instead. But if your roadmap depends on multi-chain insights updated weekly, Bitquery's 18-tool MCP turns cross-chain data wrangling into a repeatable workflow your team can run without a blockchain specialist.