timeseries-mcp

compare_series

Correlates two time series on shared timestamps and identifies the lag of strongest coupling.

Instructions

Correlate two series on shared timestamps and find the lag of strongest coupling.

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_a": "<series_a>",
      "series_b": "<series_b>"
    },
    "name": "compare_series"
  }
}

Input Schema

Name	Required	Description
`max_lag`	No	Max lead/lag (in steps) to scan.
`series_a`	Yes
`series_b`	Yes

Output Schema

Name	Required	Description
`pearson`	Yes	A single statistical test outcome.
`best_lag`	Yes	Lag (in steps, b relative to a) maximizing \|cross-correlation\|.
`series_a`	Yes
`series_b`	Yes
`spearman`	Yes	A single statistical test outcome.
`n_overlap`	Yes
`interpretation`	Yes
`ccf_at_best_lag`	Yes

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, so description must disclose behavior fully. It states the action but omits key traits: whether the operation is read-only (likely), how missing timestamps are handled, computational complexity, or side effects. The existence of an output schema helps 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.

Conciseness4/5

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

The description is a single focused sentence with no fluff. However, it may be overly terse given the tool's complexity. Front-loading the purpose is good, but additional structure (e.g., bullet points) could improve readability without much length.

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?

Despite having an output schema, the description lacks context on how timestamps are shared, what happens with missing data, or how max_lag influences results. It does not differentiate from siblings like 'autocorrelation' or 'trend_test'. Incomplete for a correlation tool.

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 low (33%: only max_lag described in schema). The description adds no parameter information for series_a, series_b, or max_lag. It fails to clarify that series_a and series_b are identifiers for time series data, leaving the agent guessing their format.

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 correlates two series on shared timestamps and finds the lag of strongest coupling. It uses specific verbs ('correlate', 'find') and resources ('two series', 'lag') and distinguishes itself from siblings like 'autocorrelation' (single series) and 'detect_anomalies' (different purpose).

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. With many sibling tools (e.g., autocorrelation, load_csv), the description should indicate scenarios like comparing two related time series to find time shifts, but it does not. No exclusions or prerequisites mentioned.

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

Deploy Server