timeseries-mcp

data_quality

Audit time series data by detecting sampling gaps, duplicate timestamps, missing values, and regularity violations.

Instructions

Audit sampling gaps, duplicate timestamps, missing values, and regularity.

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": "data_quality"
  }
}

Input Schema

Name	Required	Description	Default
`series_id`	Yes

Output Schema

Name	Required	Description
`gaps`	Yes	Largest gaps first, capped at 20.
`verdict`	Yes	One-line plain-language assessment.
`n_points`	Yes
`series_id`	Yes
`is_monotonic`	Yes	Whether timestamps are strictly increasing.
`n_gaps_total`	Yes
`missing_values`	Yes
`duplicate_timestamps`	Yes
`median_interval_seconds`	Yes
`sampling_regularity_pct`	Yes	Percent of intervals within 10% of the median interval. Null for n < 3.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 30, 2026

TDQS

C2.8/5.0

Behavior2/5

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

No annotations provided, and the description does not disclose behavioral traits such as whether the tool modifies data, requires specific data formats, or handles errors. Only lists checks.

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?

Single sentence listing the checks; concise but somewhat telegraphic. Could be rephrased for better readability.

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

Completeness3/5

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

With an output schema present, the description might suffice for basic use, but it lacks context on prerequisites, limitations, or interpretation of results.

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?

Only one parameter (series_id) with 0% schema description coverage. The description provides no details about the parameter's format, allowed values, or meaning beyond its name.

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 lists specific checks (sampling gaps, duplicate timestamps, missing values, regularity), making the purpose fairly clear. However, the verb 'audit' is vague, and it doesn't explicitly differentiate from sibling tools like detect_anomalies.

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 like detect_anomalies or describe. No when-not-to-use or context clues.

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

Deploy Server