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Align multiple signals

align_series
Read-onlyIdempotent

Resample multiple health signals onto a common time grid to compare them side by side without manual timestamp matching.

Instructions

Resample 2+ signals onto one shared time grid for side-by-side comparison.

Takes a JSON array of signal specs and returns a single aligned table — one row per time bucket, one column per signal — so signals can be compared without hand-matching timestamps.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
aggNodefault bucket aggregation ('mean','median','sum','min','max', 'first','last','count'); a spec's own "agg" overrides it.mean
joinNo'outer' (every bucket any signal has, missing entries null) or 'inner' (only buckets every signal shares).outer
userNowhich person; defaults to the primary user.
limitNomax rows returned (the most recent are kept if exceeded).
sinceNo
untilNo
resampleNo'day' | 'week' | 'month' bucket granularity.day
series_jsonYesJSON array of specs, each {"source","name"} with optional "label" and per-series "agg". Example: '[{"source":"metric","name":"weight_kg"}, {"source":"lab","name":"a1c_percent","agg":"last"}]'

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

The description fully discloses behavior: it resamples, aligns, returns a table, and mentions bucket aggregation, join types, and result limiting. It adds value beyond the readOnlyHint and idempotentHint annotations, with no contradictions.

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 two sentences, front-loaded with the main action, and every sentence adds value. It is efficient and well-structured.

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?

Given 8 parameters, 1 required, 75% schema coverage, and an output schema (not shown), the description adequately covers the tool's functionality, input format, and purpose. It is complete for effective use.

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 75% (6/8 parameters have descriptions). The description adds context about the output format and overall process but does not provide new parameter-level details beyond the schema. Baseline 3 is appropriate.

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 clearly states the tool resamples 2+ signals onto a shared time grid for side-by-side comparison, specifying the verb (resample/align), resource (multiple signals), and output (aligned table). This distinguishes it from siblings like correlate_metrics or normalize_series.

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?

The description implies usage for comparing signals without hand-matching timestamps, providing clear context. However, it does not explicitly exclude alternatives or specify when not to use this tool, which would be helpful.

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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