timeseries-mcp

describe

Get a distributional summary of a time series including quartiles, spread, skewness, kurtosis, and missing count. Understand data shape and quality at a glance.

Instructions

Distributional summary: quartiles, spread, skewness, kurtosis, missing count.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "series_id": "<series_id>"
    },
    "name": "describe"
  }
}

Input Schema

Name	Required	Description	Default
`series_id`	Yes

Output Schema

Name	Required	Description
`stats`	Yes	Five-number-style summary of the values in a series.
`kurtosis`	Yes	Excess kurtosis (normal distribution = 0).
`skewness`	Yes
`series_id`	Yes
`inferred_freq`	Yes
`last_timestamp`	Yes
`first_timestamp`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 30, 2026

TDQS

C2.9/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It lists the statistics returned, providing basic transparency, but does not mention error handling, permissions, or side effects. The output schema exists but is not referenced.

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?

One sentence, efficient and to the point. No unnecessary words or repetition.

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?

The output schema exists, so return value details are covered, but the lack of parameter explanation, usage context, and error handling leaves the description incomplete for a tool with only one parameter and no annotations.

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

Parameters1/5

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

Schema description coverage is 0%, yet the description does not mention the single parameter 'series_id'. It adds no meaning beyond the schema's type and required status.

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

Purpose4/5

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

The description clearly states it provides a distributional summary listing specific statistics (quartiles, spread, skewness, kurtosis, missing count). It distinguishes from sibling tools like data_quality or detect_anomalies, but could be more explicit about the verb (e.g., 'Calculates and returns').

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?

No guidance on when to use this tool versus alternatives. The description only states what it does without specifying prerequisites, context, or when it is preferred over other statistical tools.

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

Deploy Server