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restore_and_segment

Denoise or deblur cell images and then segment them in one pipeline. Specify restoration and segmentation models, cell diameter, and channels to output masks and the restored image.

Instructions

Restore (denoise/deblur) and segment an image in one pipeline.

Args: image_path: Path to input image restoration_model: Restoration model type (oneclick_cyto3, etc.) segmentation_model: Segmentation model type (cyto3, etc.) diameter: Expected cell diameter in pixels channels: Channel specification gpu: Whether to use GPU acceleration output_path_mask: Optional path to save masks output_path_restored: Optional path to save restored image

Returns

Dictionary with combined restoration and segmentation results

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gpuNo
channelsNo
diameterNo
image_pathYes
output_path_maskNo
restoration_modelNooneclick_cyto3
segmentation_modelNocyto3
output_path_restoredNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.5

TDQS

C2.9/5.0
Behavior2/5

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

Since no annotations are provided, the description carries the full burden. It only mentions that the tool returns a 'Dictionary with combined restoration and segmentation results' and lists parameters, but fails to disclose behavioral traits such as side effects (writing files), permissions, GPU requirements, or return structure details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficient: a one-sentence summary followed by a clearly labeled Args/Returns section. There is no fluff, and the essential information is front-loaded, making it easy to scan.

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

Completeness2/5

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

The tool has 8 parameters and an output schema (not detailed), yet the description only provides a brief mention of the return type. It lacks detail on the dictionary keys, model dependencies, or prerequisites. For a complex pipeline, this is insufficient for an agent to invoke it correctly without additional context.

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 description coverage is 0%, so the description must compensate. It provides one-line descriptions for each parameter (e.g., 'image_path: Path to input image'), which adds basic meaning, but some entries like 'channels: Channel specification' are vague. The descriptions are minimal but present.

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 clearly states that the tool restores (denoise/deblur) and segments an image in one pipeline. This is specific and differentiates from siblings that do only one task, though it does not explicitly name them.

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 guidance is provided on when to use this tool versus using separate restoration or segmentation tools. There is no mention of conditions like 'use this when both restoration and segmentation are needed' or alternatives, leaving the agent to infer the context.

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