Santiment MCP. Analyze On-Chain, Social, and Dev Metrics

You connect Santiment right into your agent, giving it instant access to deep crypto market data. It handles on-chain activity metrics, social sentiment scores, and developer development volume across thousands of assets. You can run complex analyses using natural language without ever writing a complicated API call yourself.

To start analyzing anything, you first need identifiers. The list_projects tool runs through every available crypto project and spits out a list of valid slugs. This tells your agent exactly which unique ID it needs to use when querying any metrics. You gotta run this before you can get accurate data on any coin.

When you’re ready for the numbers, you'll typically start with historical context. If you want to track how one asset has performed over time—say, checking daily active addresses or price fluctuations for Bitcoin—you use get_metric_timeseries. This tool retrieves a full time-series data set for a single metric and a specific crypto slug across any date range you define.

You ain't always looking at just one coin. If you wanna see how several assets stacked up against each other over the same period, you use get_metric_multiple_slugs. This function takes one metric and feeds it data for multiple crypto slugs in a single request. You can compare performance metrics—like market cap or total volume—across a handful of different tokens without running separate queries.

But sometimes, you don't know what you're looking for; you just know the conditions. That’s where filter_projects_by_metric comes in. This tool screens the entire market to find projects that hit specific metric thresholds. For instance, you could ask your agent to screen for all tokens with developer activity above a certain count or those showing unusual price action relative to their total market cap.

It narrows down the field instantly.

The system lets you look at institutional-grade crypto intelligence without needing deep background in data science or complex query writing. You can track historical performance metrics—like daily active addresses, circulating supply changes, or raw price movements—for single assets using get_metric_timeseries. Or, if you want to compare the health of Ethereum versus Solana side-by-side, you run get_metric_multiple_slugs.

If your goal is market discovery, filter_projects_by_metric is key. You can use it to identify opportunities—maybe finding assets with high on-chain volume but low social media mention counts. It lets you test specific criteria against the whole asset universe. Remember, every piece of data you pull starts with a valid slug from list_projects.

This structured approach means your agent always works with accurate identifiers, making the entire process reliable and fast.