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Glama

compare_windows

Compares median brightness in two time windows for a selected series and reports the significance of the difference, helping identify real changes in time-domain data.

Instructions

Median brightness of a series in window A vs window B, with the significance of the difference.

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": {
      "case": "<case>",
      "series": "<series>",
      "a_start": "<a_start>",
      "a_end": "<a_end>",
      "b_start": "<b_start>",
      "b_end": "<b_end>"
    },
    "name": "compare_windows"
  }
}

Input Schema

NameRequiredDescriptionDefault
caseYes
a_endYes
b_endYes
seriesYes
a_startYes
b_startYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.1

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 disclosure burden. It does add genuinely useful behavior beyond structured fields by revealing that the result includes a statistical significance measure, but it says nothing about read-only nature, time-format expectations, or how degenerate/overlapping windows are handled.

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?

A single front-loaded sentence with zero filler and the core computation stated first. It is efficient, though the terseness borders on under-specification rather than clean conciseness.

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?

For a tool with six required parameters, no annotations, and no output schema, the description is thin. An agent knows roughly what will be computed but not the units/formats of the window bounds, what 'case' refers to, or the shape of the returned significance value.

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?

Schema coverage is 0%, so the description must compensate. It implies that 'series' names the data series and that windows A and B map to the a_start/a_end and b_start/b_end pairs, but it never explains the 'case' parameter or the number-vs-string time formats allowed by the schema.

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?

States a specific computation: compares median brightness of a series between two named windows and reports the significance of the difference. This distinguishes it from siblings like find_excursions or cross_band, though it never names an alternative explicitly.

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?

There is no when-to-use guidance, no indication of prerequisites (e.g., that a case and series must already exist), and no mention of when a different comparison tool would be more appropriate. The agent must infer context entirely.

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