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get_imaging_metadata

Retrieve parsed imaging metadata from N.I.N.A. session folders, including frame type, date, source, and target details. Filter by image type (light, dark, bias, flat) and optional date for quality checks.

Instructions

Returns imaging metadata parsed from the ImageMetaData.csv files in the frame folders (LIGHT, DARK, BIAS, FLAT, etc.) of the mounted N.I.N.A imaging directory. Each row is tagged with Date, FrameType, and Source. When an AcquisitionDetails.csv is present in the same session directory, its fields (e.g. TargetName, FocalLength) are injected into each image row unless the image row already has a value for that field. Defaults to lights frames for star-quality checks; pass another image type (light, dark, bias, flat — case-insensitive). Optionally filter to a single YYYY-MM-DD date.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNo
image_typeNolight

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses the merging behavior (injecting AcquisitionDetails.csv fields unless already present), the default image type, and case-insensitivity. It does not explicitly state read-only safety, but the verb 'Returns' implies it, and it adds valuable behavioral context about the data transformation.

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 multi-sentence paragraph but every sentence carries useful information: source, tagging, injection rule, defaults, and filter. It is front-loaded with the main purpose and does not waste words, though it could be slightly more compact without losing substance.

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?

For a read tool with a documented output schema, the description covers the essential behaviors: source location, row tagging, field injection, default frame type, and date filter. It does not mention error cases or what happens if the directory is missing, but those are minor given the output schema and the tool's read-only nature.

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 description coverage is 0%, so the description must compensate. It explains both parameters: image_type with allowed examples and case-insensitivity, and date with a specific YYYY-MM-DD format. This adds meaning well beyond the bare schema and provides concrete guidance for values and 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 verb 'Returns' and the resource 'imaging metadata parsed from the ImageMetaData.csv files', and specifies the source directory and file type. It also distinguishes itself from siblings (which are about site status, sequences, logs) by being the only tool focused on imaging metadata extraction.

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

Usage Guidelines4/5

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

The description gives clear context: it defaults to lights frames for star-quality checks and allows passing other image types, and optionally filtering by date. However, it does not explicitly name alternative tools or state when not to use it, so it lacks explicit exclusions but provides enough situational context.

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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