Reduce image noise in a PixInsight view by running NoiseXTerminator with configurable denoise strength and detail preservation.
Run NoiseXTerminator to reduce noise on a view.
| Name | Required | Description | Default |
|---|---|---|---|
| detail | No | Detail preservation, 0 to 1. | |
| denoise | Yes | Denoise strength, 0 to 1. | |
| view_id | Yes | PixInsight view identifier. | |
| ml_version | No | Neural network model version (the process's ml_version parameter). Omitted, the process keeps its own setting. |
Changes observed during successful MCP inspections.
v2.5.0Input schema / properties / ml_versionAdded value: +{
+ "description": "Neural network model version (the process's ml_version parameter). Omitted, the process keeps its own setting.",
+ "type": "number"
+}v2.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It says the tool reduces noise on a view but does not disclose that this is a mutation, whether it modifies the view in place or returns a new one, whether it is reversible, or any resource/performance characteristics. This is a significant gap for a processing tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence with no filler or redundancy. It is efficient, though arguably under-specified rather than tightly scoped.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a four-parameter processing tool with no output schema and no annotations, the description is too sparse. It does not explain the return/result behavior, in-place mutation, or workflow context, leaving the agent to infer how to apply it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so all four parameters (detail, denoise, view_id, ml_version) are documented in the schema with ranges and defaults. The description adds no parameter meaning beyond that, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb ('Run') and resource ('NoiseXTerminator') plus the effect ('reduce noise on a view'), so the operation is unambiguous. However, it offers no differentiation from the many other run_* sibling tools (run_bxt, run_sxt, run_pjsr, run_scnr, etc.), so an agent must know NoiseXTerminator by name to place it.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this denoise step versus alternatives like run_scnr or run_pjsr, no prerequisites, and no ordering advice (e.g., where in a workflow it belongs). The agent gets only the purpose, not the context for selecting it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.