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[{"name":"metric","type":"string","required":false,"description":"Santiment metric name (e.g., price_usd, daily_active_addresses, mvrv_usd)"},{"name":"slug","type":"string","required":false,"description":"Asset identifier (e.g., bitcoin, ethereum, cardano)"},{"name":"from","type":"string","required":false,"description":"Start of time range (ISO 8601 or relative expression like utc_now-7d)"},{"name":"to","type":"string","required":false,"description":"End of time range (ISO 8601 or relative expression like utc_now)"},{"name":"interval","type":"string","required":false,"default":"1d","description":"Data granularity (5m, 1h, 1d, 7d)"}]
prerequisites
{"env_vars":["SANTIMENT_API_KEY"],"skills":[]}
composable
true
persist_state
false
scripts
{"enabled":true,"working_directory":"./scripts","definitions":[{"name":"santiment_query","description":"Execute a Santiment GraphQL query for any metric, asset, and time range","type":"python","file":"santiment_query.py","timeout":30},{"name":"santiment_discovery","description":"Discover available metrics and asset slugs from the Santiment API","type":"python","file":"santiment_discovery.py","timeout":30}]}
Query the Santiment API — a GraphQL platform providing 750+ metrics for 2,000+ crypto assets across 12 blockchains. Fetch on-chain, financial, social, and development data for the cryptocurrency market.
Every request requires an API key in the Authorization header (Authorization: Apikey <KEY>). The user must provide their own key — never hardcode or assume one. If $SANTIMENT_API_KEY is set in the environment, use it directly. Otherwise, ask the user for their key (free tier available at https://app.santiment.net/account#api-keys).
All requests are HTTP POST with Content-Type: application/json. Use GraphQL variables to separate the query template from runtime values — this avoids quote-escaping errors. In curl, use a heredoc with a quoted delimiter (<< 'QUERY') so the shell doesn't interpret $variable as environment variables.
Use these GraphQL types in query(...) variable declarations:
Parameter
Type
metric
String!
slug
String
selector
MetricTargetSelectorInputObject
from / to
DateTime!
interval
interval
aggregation
Aggregation
transform
TimeseriesMetricTransformInputObject
Core Query: getMetric
Nearly all data flows through getMetric. Pass a metric name string and then select one sub-field that determines the shape of the response data.
Sub-fields
Choose exactly one sub-field per getMetric call:
Sub-field
Returns
When to use
timeseriesData
[{datetime, value}]
Default choice — returns a time series with explicit field selection. Use for charting, trend analysis, or any case where individual data points matter.
timeseriesDataJson
Pre-formatted JSON string
Quick dump without specifying return fields. Useful for rapid prototyping but less control over output shape.
timeseriesDataPerSlugJson
JSON object keyed by slug
Fetch the same metric for multiple assets in a single API call. Pass selector: { slugs: ["bitcoin", "ethereum"] }. This counts as one API call, making it much more efficient than separate queries per asset.
aggregatedTimeseriesData
Single numeric value
Need one summary number (average, sum, last value) over a time range. Lowest complexity, most quota-friendly.
histogramData
Array of bucketed ranges
Distribution data — e.g., how much ETH was bought at each price range. Less commonly needed.
metadata
Metric metadata object
Discover available slugs, aggregations, selectors, intervals, and data type for a metric. Call this before querying an unfamiliar metric.
availableSince
ISO 8601 date string
Check how far back data exists for a given metric and slug. Prevents wasted calls on empty time ranges. A return value of 1970-01-01T00:00:00Z means the metric has never been computed for this slug.
lastDatetimeComputedAt
ISO 8601 timestamp
Check data freshness — when the metric was last computed for a slug.
Parameters
These parameters are passed to the sub-field, not to getMetric itself:
Parameter
Type
Description
Example
slug
String
Asset identifier. Use for single-asset queries.
"bitcoin", "ethereum"
selector
Object
Advanced selection. Use instead of slug when you need multi-asset queries, or filtering by owner, label, holdersCount, or source.
{ slug: "bitcoin", source: "cryptocompare" }
from
String
Start of time range. Accepts ISO 8601 timestamps or relative expressions.
"2024-01-01T00:00:00Z", "utc_now-30d"
to
String
End of time range. Same format as from.
"utc_now"
interval
String
Time granularity between data points.
"5m", "1h", "1d", "7d"
aggregation
Enum
Override the default aggregation method. Options: AVG, SUM, LAST, FIRST, MEDIAN, MAX, MIN, ANY.
AVG
transform
Object
Post-processing transform applied to the result.
See Transforms section.
Important:slug and selector are mutually exclusive. Use slug for simple single-asset queries. Use selector when querying multiple slugs at once ({ slugs: [...] }) or when a metric requires additional selector fields like holdersCount or owner.
Relative Time Expressions
The from and to fields accept relative expressions: utc_now-<N><unit> where unit is d (day), w (week), m (month), or y (year). Preferred over hardcoded dates.
"utc_now" — current time
"utc_now-7d" — 7 days ago
"utc_now-1m" — 1 month ago
"utc_now-1y" — 1 year ago
Transforms
Apply transforms directly in the query to post-process data server-side. Pass the transform parameter to timeseriesData:
Transform
Effect
Use case
{type: "moving_average", movingAverageBase: N}
Replace each value with the average of the preceding N values
Smooth noisy metrics like MVRV, social volume
{type: "consecutive_differences"}
Replace each value with the difference from the prior value
Compute daily change from a cumulative metric
{type: "percent_change"}
Replace each value with the percent change from the prior value
Normalize changes across metrics with different scales
Transforms reduce the number of returned data points by N-1 for moving averages. Request a slightly wider time range to compensate.
Dynamic Discovery
When you don't know the exact metric name for a user's request, follow this 3-step workflow. Always prefer dynamic discovery over hardcoded values.
Step 1 — Fetch the metric list
{
getAvailableMetrics
}
Returns 1,000+ snake_case strings. The response is large — save to a file with -o and read it directly (e.g., open() in Python). Do not pipe the contents through stdin at any stage — neither from curl nor when processing the file afterward:
Filter by plan with getAvailableMetrics(product: SANAPI, plan: BUSINESS_PRO).
Step 2 — Search by keywords
Search the saved file for keywords matching user intent. Example: whale activity → search for holder, top, whale, supply, amount_in. See references/metrics-catalog.md for intent-to-keyword mappings.
Step 3 — Inspect metadata before querying
Once you have candidate metrics, fetch metadata to confirm compatibility:
This reveals required selectors (e.g., holdersCount), supported slugs, and minimum interval. Requesting a smaller interval than minInterval returns an error. See examples/query-patterns.md example 5 for a worked example.
Find a project's slug
Look up a specific project by slug to verify it exists and see its details:
{
projectBySlug(slug:"bitcoin"){
slug
name
ticker
infrastructure
}}
Browse all projects paginated (useful when the user provides a token name or ticker instead of a slug):
{
allProjects(page:1, pageSize:50){
slug
name
ticker
}}
Check data availability for a metric and slug
Before making a large timeseries query, verify that data exists for the desired metric-slug combination and check freshness:
The API always returns HTTP 200, even for errors. Always parse the JSON and check for the errors array.
A typical error response:
{"data":{"getMetric":null},"errors":[{"message":"The metric 'nonexistent_metric' is not a valid metric.","locations":[{"line":2,"column":3}]}]}
When data contains null and errors is present, the query failed. Common errors:
Error
Cause
Fix
Invalid metric name
Metric string doesn't exist
Verify with getAvailableMetrics
Invalid slug
Asset slug not recognized
Verify with allProjects or projectBySlug
Interval too small
Requested interval below minimum for this metric or plan
Check metadata { minInterval }
Time range restricted
Historical range exceeds plan allowance
Check getAccessRestrictions
Complexity too high
Query requests too many data points
Reduce time range, increase interval, or fetch fewer fields
HTTP 429
Rate limit exceeded
Back off exponentially and retry after a delay
Empty timeseries (no error)
On-chain metric not computed for this asset's chain
Check availableSince for epoch, then projectBySlug { infrastructure }. See references/metrics-catalog.md Ghost Data section.
Empty data is not the same as zero data. When timeseriesData returns [] without errors, do NOT tell the user there is no activity. On-chain metrics are only computed for indexed chains. Check references/metrics-catalog.md Ghost Data section for the diagnostic flow.
The one exception to the HTTP 200 rule is rate limiting: HTTP 429 means the account's quota is exhausted. See references/rate-limits.md for details.
Quick Reference: Building a Query
Follow these steps to construct any Santiment API query:
Pick a metric — use directly if known, otherwise follow Discovery Workflow.
Pick a slug — resolve names/tickers via projectBySlug or allProjects.
Pick a time range — relative expressions preferred. Check availableSince first.
Pick a sub-field — timeseriesData for series, aggregatedTimeseriesData for single value, timeseriesDataPerSlugJson for multi-asset.
Optionally add — transform, selector (instead of slug), or aggregation override.
If data is empty — check availableSince for epoch. If epoch, the metric isn't computed for this slug's chain. Report unavailability and suggest alternative metrics.
Execute via curl with the user's API key and parse the JSON response. Always check for the errors array before processing data.
Additional Resources
Consult these reference files for detailed information:
Metrics catalog — references/metrics-catalog.md — keyword-to-metric mapping for translating user intent into search terms, plus 20 curated quick-reference metrics and naming conventions
Rate limits — references/rate-limits.md — tier limits, complexity scoring, and optimization strategies to avoid quota exhaustion
Query patterns — examples/query-patterns.md — 6 worked examples with both GraphQL and curl, including discovery workflow and ghost data diagnostics
