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Python
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data_pixel_sample

Verify a TOP image's luma statistics (mean/min/max/std) to detect dark, solid, or clipped output using configurable thresholds.

Instructions

Luma statistics of a TOP (mean/min/max/std) with dark / solid / clipped flags. The 'is the output actually black?' verifier.

path (<class 'str'>): TOP operator path.

grid (int | None): Samples per axis (default 16).

numpy (bool | None): Full-frame read via numpyArray().

dark_below (float | None): Mean luma below this = dark (0.02).

solid_std (float | None): Std below this = solid (0.005).

detail (str | None): full (default) | summary (long lists cut to 25 + count) | minimal (top-level scalars only).

response_format (str | None): yaml (default, token-cheap) | json.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gridNo
pathYes
numpyNo
detailNo
solid_stdNo
dark_belowNo
response_formatNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.4.0
    • addedInput schema / properties / detail
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Detail"
+}
    • addedInput schema / properties / response_format
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Response Format"
+}
  2. First observedv0.2.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations provided, the description carries the full disclosure burden. It indicates a read operation ('full-frame read via numpyArray()') and describes the statistical computation, but it does not explicitly state side effects (e.g., whether it is read-only), or any potential performance implications. It is adequate for a sampling tool but lacks explicit safety declarations.

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 opens with a clear one-line purpose, then presents a compact parameter list with inline explanations. It is front-loaded and each sentence adds value. Slightly long, but the parameter details are necessary given the lack of schema descriptions.

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?

There is no output schema, so the description must convey what the tool returns. It names the statistics (mean/min/max/std) and flags, but does not describe the result structure (e.g., a dictionary with specific keys) or potential error cases. It is sufficient for a basic call, but leaves some ambiguity about the exact response shape and handling of edge cases like missing path.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description provides meaningful context for every parameter, including defaults (16, 0.02, 0.005), interpretation (dark_below, solid_std, detail levels), and output format choices (yaml/json). Since schema description coverage is 0%, this textual parameter documentation is essential and largely compensates for the schema's lack of descriptions.

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 description states a specific verb and resource: it computes luma statistics (mean/min/max/std) of a TOP and produces dark/solid/clipped flags. The phrase 'is the output actually black?' verifier adds a concrete use case. However, it does not explicitly distinguish itself from sibling tools like data_top, so it misses the top tier of sibling differentiation.

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

Usage Guidelines3/5

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

The 'verifier' phrase implies a use case (checking if an output is effectively black or uniform), but there is no explicit guidance on when to prefer this tool over siblings such as data_top or data_sop, nor any exclusions or prerequisites. The usage context is implied but not spelled out.

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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