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by Alexnikolaev1
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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analyze_series

Detect explainable anomalies and recurring patterns in a numeric time series with Matrix Profile, showing each result's nearest match for context.

Instructions

Find explainable anomalies and recurring patterns in a time series using the Matrix Profile algorithm. Each anomaly is explained by showing its nearest match elsewhere in the series (or the lack of one), not just flagged as a bare score.

Args: values: The time series as a flat list of numbers, in order. subsequence_length: The length of the pattern window to compare (e.g. 12 for a daily cycle sampled hourly). Must be at least 3 and at most half the series length. top_k: How many top anomalies/patterns to report.

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": {
      "values": "<values>",
      "subsequence_length": "<subsequence_length>"
    },
    "name": "analyze_series"
  }
}

Input Schema

NameRequiredDescriptionDefault
top_kNo
valuesYes
subsequence_lengthYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it explains that anomalies are explained by their nearest match rather than returned as bare scores, and it states validation constraints (subsequence_length must be >= 3 and <= half the series length). It stops short of disclosing determinism, performance characteristics, or failure behavior on short/constant series.

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 core behavioral claim is front-loaded in the first two sentences, and the Args block is compact with no redundant restatement of the property names. Every sentence carries information.

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?

An output schema exists, so the description is not obliged to enumerate return fields, and it already sketches the shape of results (anomalies plus recurring patterns with nearest-match explanations). Parameter semantics and constraints are complete; the only real gap is guidance on choosing between this and the sibling analyze_csv.

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

Parameters5/5

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

Schema coverage is 0%, so the description must carry all parameter meaning, and it does: values is defined as a flat, ordered list; subsequence_length is given an intuitive example (12 for a daily cycle sampled hourly) plus hard lower and upper bounds; top_k is defined as how many anomalies/patterns to report. This fully compensates for the empty schema descriptions.

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 uses a specific verb+resource pair ('Find explainable anomalies and recurring patterns in a time series') and even names the algorithm (Matrix Profile), which tells an agent exactly what class of analysis this performs. It does not explicitly differentiate from its sibling analyze_csv, though the 'time series' framing makes the boundary reasonably inferable.

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

Usage Guidelines3/5

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

Usage is implied: you supply an ordered list of numbers and a window length to detect anomalies and recurring patterns. There is no statement of when to prefer this over analyze_csv or when this approach is inappropriate (e.g. non-numeric or unordered data), leaving the routing decision to inference.

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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