oddsky

observer_agreement

Compares event depths for each AAVSO observer or camera against their own baselines to verify independent agreement and cancel zero-point offsets.

Instructions

Does every independent observer see the same thing? For each AAVSO observer (or camera) with data in both windows: their own baseline median, their faintest and median point in the event window, and the depth relative to their own baseline. Observer zero points cancel.

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>",
      "baseline_start": "<baseline_start>",
      "baseline_end": "<baseline_end>",
      "start": "<start>",
      "end": "<end>"
    },
    "name": "observer_agreement"
  }
}

Input Schema

Name	Required	Description	Default
`end`	Yes
`case`	Yes
`start`	Yes
`series`	Yes
`min_points`	No
`baseline_end`	Yes
`baseline_start`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.1Nov 12, 2026

TDQS

B3.4/5.0

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 it does substantial work: it discloses that only observers with data in both windows are included, that statistics are computed per observer against their own baseline, and that zero-point offsets cancel. It still omits failure behavior, ordering of results, and what happens when no observer qualifies.

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?

Three sentences, front-loaded with the central question and then the output definition. No filler, though the interrogative framing is slightly less efficient than a direct verb-led statement of what the tool returns.

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?

For a 7-parameter, 6-required analysis tool with no annotations and no output schema, the description usefully explains the returned fields but leaves the input contract underspecified, especially min_points and date formatting. It is adequate to understand the purpose yet incomplete for confident invocation.

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 0% across 7 parameters, so the description must compensate. It hints at the two-window structure (baseline vs event) and at per-observer grouping, but never maps these to the named arguments, never explains date formats, and completely omits min_points, which has a default of 3 and materially affects which observers appear.

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 opening question plus the enumeration of computed statistics makes clear this tool measures per-observer consistency: each observer's own baseline median, their faintest and median point in the event window, and depth relative to that baseline. That is specific enough to distinguish it from generic comparison tools, but it never names or contrasts a sibling such as compare_windows, so sibling differentiation is left to inference.

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 explicit guidance on when to reach for this tool versus alternatives like compare_windows or cross_band, and no stated prerequisites or exclusions. A reader can infer it is a consistency/vetting check across observers, but the choice between this and nearby siblings is unstated.

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