timeseries-mcp

detect_anomalies

Identify and score outliers in time-series data using statistical methods (z-score, MAD, IQR, or seasonal STL residuals). Returns anomalies ranked by severity.

Instructions

Flag anomalous observations; returns scored anomalies, strongest first.

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": "detect_anomalies"
  }
}

Input Schema

Name	Required	Description	Default
`method`	No	zscore/mad/iqr are global; stl_residual is seasonal-aware (needs period).	zscore
`period`	No	Seasonal period, required for stl_residual.
`series_id`	Yes
`threshold`	No	Score cutoff (zscore/mad/stl) or IQR fence multiplier (iqr).

Output Schema

Name	Required	Description
`notes`	Yes
`method`	Yes
`anomalies`	Yes	Highest scores first, capped at 50.
`series_id`	Yes
`threshold`	Yes
`n_anomalies`	Yes
`baseline_std`	Yes
`baseline_mean`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 30, 2026

TDQS

A3.6/5.0

Behavior3/5

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

No annotations provided, so description carries full burden. It states the output structure (scored, sorted) but does not mention data requirements (e.g., numeric series, missing value handling) or side effects.

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 a single sentence that efficiently conveys purpose and output format with no redundant words.

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?

Given the complexity (4 parameters, output schema exists), the description plus schema cover the core functionality. Minor omission: no mention that input must be numeric, but this is implied by anomaly detection.

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% and already provides detailed parameter descriptions (method enum, period condition, threshold meaning). The main description adds no new parameter information beyond what schema offers.

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 flags anomalous observations and returns scored anomalies sorted by strength, which distinguishes it from siblings like detect_changepoints.

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 main description lacks explicit guidance on when to use this tool vs alternatives. The schema mentions method-specific distinctions (global vs seasonal-aware), but no systematic when-to-use criteria.

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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