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mxcoppell

pixinsight-mcp

by mxcoppell
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JavaScript
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measure_stars

Measure star size, brightness, and color in a view. Returns median FWHM, peak signal, background contrast, and star counts from pixel sampling.

Instructions

Measure the stars of a view by pixel sampling. Candidates are local maxima of luminance found by a 16 px grid scan above median + 5 x MAD, refined within 5x5, de-duplicated within 20 px (at most 100 kept); the 30 brightest are measured. FWHM of a star = 2 x the mean radius, over the four axis directions (up to 10 px), where luminance drops below half its peak; colour diversity of a star = max - min of its peak RGB divided by the largest channel. Returns JSON: median_fwhm_px, color_diversity (median), stars_found, stars_measured, median_peak, p25_peak, background_median, star_background_contrast (median_peak / background_median, 0 when background_median <= 0.001), and up to 10 samples of each. luminance is 0.2126R + 0.7152G + 0.0722B.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
view_idYesView to measure

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.2.1

TDQS

A4/5.0
Behavior5/5

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

With no annotations, the description fully discloses the tool's behavior: pixel sampling grid size, local maxima criteria, deduplication limits, star count caps, FWHM definition, color diversity formula, luminance weights, and response fields including edge-case behavior for background_median. This is exemplary transparency.

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 dense but front-loaded with the core purpose before diving into technical specifics. Every detail (algorithm, limits, output fields) serves a purpose, though it is a single long block rather than structured sections, so it loses one point.

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

Completeness5/5

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

Given the tool's complexity and the absence of an output schema, the description is remarkably complete. It names every returned JSON field, explains how each is computed, states invariants like 'at most 100 kept' and the '30 brightest measured', and defines edge cases. An agent has enough information to invoke it correctly and interpret results.

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?

There is only one parameter, view_id, and the schema already covers it at 100% with 'View to measure.' The description does not add significant new semantic detail about how view_id is used beyond the schema, so the baseline score of 3 is appropriate.

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 opens with a specific verb and resource: 'Measure the stars of a view by pixel sampling.' It explains the exact algorithm and output stats, making it easy to distinguish from sibling measurement tools like measure_star_layer or measure_ringing.

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 explicit guidance is given about when to use this tool versus alternatives such as measure_star_layer or measure_sharpness. The description is purely algorithmic and does not mention when it is the appropriate choice, nor when another measurement tool should be preferred.

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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