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Lkhanaajav

timeseries-mcp

by Lkhanaajav

resample

Convert time series to regular intervals using a pandas offset alias. Aggregate data with mean, sum, min, max, median, or count.

Instructions

Resample onto a regular grid; registers and returns a NEW derived series.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
aggNoAggregation.mean
ruleYesPandas offset alias, e.g. '5min', '1h', '1D'.
series_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
endYesISO-8601 timestamp of the last observation.
nameYes
startYesISO-8601 timestamp of the first observation.
statsYesFive-number-style summary of the values in a series.
sourceYesWhere the series came from: csv path, inline, sample, or a derivation.
n_pointsYes
series_idYes
inferred_freqYesPandas frequency string inferred from the index, e.g. '5min'; null if irregular.
Behavior3/5

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

With no annotations, the description must disclose behavioral traits. It states the tool registers and returns a new derived series (non-destructive), but does not elaborate on permission needs, missing data handling, or side effects. Adequate but not rich.

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

Conciseness4/5

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

The description is a single concise sentence. It front-loads the primary action. However, the term 'registers' is slightly vague and could be clarified.

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

Completeness2/5

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

Given the tool resamples time series, more context is needed: what the output contains (e.g., timestamps, values), how missing data is handled, and the effect of the 'agg' parameter. The presence of an output schema helps but is not visible here. The description is incomplete for a 3-param tool with 2 required.

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

Parameters2/5

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

Schema coverage is 67% (agg and rule have descriptions in schema). The tool description adds no additional meaning beyond the schema. The series_id param lacks contextual explanation. The description does not compensate for the 33% uncovered param.

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 verb 'resample' and the resource 'series onto a regular grid', with the added distinction that it 'returns a NEW derived series', which differentiates it from modifying tools. Sibling tools like load_csv, describe, etc. are distinct.

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

Usage Guidelines2/5

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

The description provides no explicit guidance on when to use resample vs alternatives (e.g., rolling_stats, get_window). It does not mention prerequisites, when not to use, or preferred scenarios.

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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