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Santiment

by io.github.santiment

Fetch Metric Data

fetch_metric_data_tool
Read-only

Fetch metric timeseries for one metric and one or many slugs.

Defaults: last 30 days (time_period="30d"), interval="1d".

Use this when the assets are already known and the values over time matter. For the opposite direction — "which assets satisfy X" / "top N by X", one aggregated value per asset across the whole universe — use assets_by_metric_tool. To confirm a metric exists for a slug first, use metrics_and_assets_discovery_tool; to draw the result, use show_chart.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugsYesList of slug identifiers (e.g., ["bitcoin"], ["bitcoin", "ethereum"], etc.). Accepts at most 10 slugs at a time. Only metrics that have `supports_many_slugs: true` can accept more than one slug. Check the `supports_many_slugs` field in the metrics_and_assets_discovery_tool response before passing multiple slugs. Financial and on-chain metrics generally support multiple slugs; social, sentiment, and derivatives metrics generally do not. The tool returns data for one metric and one or many slugs.
metricYesMetric name to fetch (e.g., 'price_usd'). IMPORTANT: Before fetching data, verify metric names by calling the metrics_and_assets_discovery_tool first. Only metrics listed there are supported. Do not guess or infer metric names — they may differ from what you expect.
intervalNoThe interval between two data points in the timeseries data (e.g., '5m', '1h', '1d'). The format is: <number><suffix>, where: - <number> is an integer - <suffix> is one of: - m (minutes) - h (hours) - d (days) - w (weeks) - y (years) For example, 5m means that the data returned will have a 5 minute interval between two data points. Each metric has predefined `min_interval`. It describes the lowest possible interval for which data is available. If the metric has `min_interval=1d` it means that Santiment has one data point per day for that metric. For these metrics `interval="5m"` won't work as 5 minutes is less than 1 day.
time_periodNoHow far back in time to fetch the data for (e.g., '7d', '30d', '90d'). This parameter defines the range of metric data to fetch - from <time_period> time ago up until now. Defaults to 30d.

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover read-only/non-destructive behavior, lowering the bar. The description adds meaningful context: default time range (30d) and interval (1d), and clarifies this returns per-asset time series rather than a single aggregated value. It does not mention response format or pagination, but that is a minor gap given the annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded: the first sentence states the core action, followed by defaults, then usage guidance and sibling references. Every sentence earns its place with no repetition or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 4-parameter read-only tool with rich schemas, the description covers purpose, defaults, when-to-use, alternatives, prerequisites, and next steps. However, since there is no output schema, it omits the return shape or pagination details, which would further aid invocation confidence.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with detailed descriptions for all four parameters, so the baseline is 3. The description adds default values for `time_period` and `interval`, but otherwise does not go beyond the schema's already-rich parameter explanations.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'Fetch metric timeseries for one metric and one or many slugs' — a specific verb, resource, and clear scope. It also distinguishes itself from siblings by explicitly contrasting with `assets_by_metric_tool`'s aggregated, universe-level use case.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit when-to-use: 'Use this when the assets are already known and the values over time matter.' It names the exact alternative for the opposite direction (`assets_by_metric_tool`), and provides a prerequisite (`metrics_and_assets_discovery_tool`) and follow-up (`show_chart`) tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: screening assets vs fetching timeseries vs catalog discovery vs charting vs insights discovery/fetch vs trending stories vs combined trends. The overlapping trending tools are explicitly differentiated through a superset relationship with usage guidance.

Naming Consistency3/5

All names are snake_case and most end in '_tool', but the pattern is mixed: some start with verbs (fetch_*, show_*) while others are noun phrases (assets_by_metric_tool, combined_trends_tool, insight_discovery_tool). This is readable but not a consistent verb_noun convention.

Tool Count5/5

8 tools is well-scoped for a crypto analytics server, covering discovery, data retrieval, screening, charting, and content access without bloat or thinness. Each tool earns its place in the workflow.

Completeness4/5

The toolset covers the core workflows: catalog discovery, metric timeseries, asset screening, chart rendering, insights list/fetch, and trending data. Minor gaps include no multi-metric timeseries fetch and a limited set of chart overlay options, but agents can work around these.

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