Image Label MCP Server

Check slide quality

check_image_quality

Read-onlyIdempotent

Assess histology image quality before analysis by measuring tissue coverage, blur, marker-pen ink, exposure, and faint staining, then returning a good/check/poor verdict with warnings.

Instructions

Check a histology image's quality before analysing it (HistoQC-style checks).

Measures how much of the image is tissue, blur, marker-pen ink, over- and under-exposure and faint staining. Returns a verdict (good, check or poor), the measurements and plain-language warnings.

Input Schema

Name	Required	Description	Default
`image_id`	Yes	Id of an image in Image Label: the imageId (a UUID string) returned by list_images or an upload.

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv0.8.1Oct 28, 2026

addedInput schema / properties / image_id / description

Added value: +"Id of an image in Image Label: the imageId (a UUID string) returned by list_images or an upload."

Addedv0.3.3Oct 24, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare read-only, idempotent and non-destructive behavior. The description adds useful behavioral detail by listing the measured quality dimensions and explaining that results include a verdict, measurements and plain-language warnings.

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 three concise sentences, front-loaded with the core action and followed by measurement details and return information. Every sentence contributes useful information without repetition.

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?

For a single-parameter, read-only quality check with an output schema and complete annotations, the description provides enough context to invoke it correctly. It explains what is measured and what kind of result to expect.

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 one required parameter and schema coverage is 100%, so the schema fully documents image_id. The description does not add meaning beyond the schema, making the baseline score appropriate.

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?

States a specific verb and resource: checking a histology image's quality before analysis. It clearly describes the quality dimensions measured and the returned verdict, though it does not explicitly name a sibling tool such as analyze_slide for differentiation.

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?

Gives clear usage context: run this before analysing an image. It implies the pre-analysis workflow but does not specify when not to use it or explicitly compare it with analyze_slide or get_slide_analysis.

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