Kairos Signal — verifiable DePIN data API

Exact day/unit Shapley attribution of a network's daily moves (paid)

get_shap_node_attribution

Destructive

Paid: 1.5 credits ($1.50) per answered call, charged once and only after a non-empty answer is computed; an unavailable answer, a refusal or an error costs nothing. Send your API key in the X-API-Key header or as api_key. Each call is a new answer and a new charge (no idempotency key). For one DePIN network: an exact two-factor functional ANOVA of the daily log changes of its stored first-party metrics over the last days complete UTC days (same input rules as get_rmt_regime_state), with factors day (moves shared by the network's metrics that day) and unit (metrics measured in the same unit; units held by one metric are pooled into one named group; a panel where a group would hold one metric, or that has only one unit group, is answered unavailable, uncharged). Returns each factor's variance share, their interaction and the metric-specific remainder, the mean absolute Shapley value per factor, and for the latest day each metric's change split exactly into its day and unit Shapley values plus remainder (additivity error reported). Descriptive: not causal, not a forecast. Uses up to max_metrics (3-60, default 30) metrics, by name order. Every input daily close is returned with its as_of and verify_url.

Input Schema

Name	Required	Description
`days`	No	Daily changes in the window (default 30), ending at the latest complete UTC day.
`symbol`	Yes	One network ticker, e.g. AKT, FIL, RENDER.
`api_key`	No	Your API key, only if your client cannot send the X-API-Key header.
`max_metrics`	No	Metrics used (default 30).
`metric_filter`	No	Optional case-insensitive substring of metric names, e.g. GPU.

Output Schema

Name	Required	Description	Default
`tool`	No
`result`	Yes
`schema`	Yes
`status`	Yes
`symbol`	Yes
`call_id`	No
`credit_cost`	Yes
`charge_state`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 23, 2026
RemovedOct 23, 2026
AddedOct 22, 2026

TDQS

A4.3/5.0

Behavior5/5

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

Annotations only carry readOnlyHint=false, idempotentHint=false and a generic destructiveHint=true; the description supplies the actual behavior an agent needs — 1.5 credits per answered call, charged only on a non-empty answer, free on unavailability/refusal/error, no idempotency key, auth via X-API-Key or api_key, and a caps on metric count. It also states the results are descriptive, not causal or a forecast, and that additivity error is reported.

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

Conciseness3/5

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

Cost and charge semantics are correctly front-loaded, but the middle of the description is a single dense run-on sentence with parenthetical nesting about unit pooling and unavailable panels that is hard to parse. The content mostly earns its place, but it is not structured for scanning.

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

Completeness5/5

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

For a paid, non-idempotent analytical call this covers everything an agent needs: price and charging trigger, auth paths, refusal/unavailable semantics, metric-count limits, and the shape of what is decomposed. An output schema exists, and the description still briefly names the returned quantities and additivity error, so nothing material is missing.

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%, so days, symbol, max_metrics, api_key and metric_filter are all documented in the schema itself, which sets the baseline at 3. The description adds only marginal meaning beyond that (metric selection is 'by name order', max_metrics 3-60 default 30) and never mentions metric_filter, the one parameter most likely to need extra explanation.

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 a precise verb and resource: an exact two-factor functional ANOVA / Shapley decomposition of daily log changes for one DePIN network, with named factors (day, unit) and named outputs. It also explicitly anchors itself to a sibling ('same input rules as get_rmt_regime_state'), so an agent can separate it from get_rmt_regime_state and get_metric_history.

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

Usage Guidelines4/5

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

It gives clear context: one network at a time, input rules inherited from get_rmt_regime_state, and when-not conditions (a unit group holding a single metric, or only one unit group, yields an unavailable answer). It does not spell out the alternative to pick when you want raw metric history versus this attribution, so the routing is implied rather than stated.

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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