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list_generated_images

List previously generated images in the project's generated-assets directory with paths, dimensions, file sizes, and prompts. Use at session start to discover reusable assets for editing.

Instructions

List the images this server has written to the project's generated-assets directory (IMAGE_OUTPUT_DIR, default ./assets/generated): absolute path, pixel dimensions, file size in KB, and the prompt that produced each one when its .json sidecar exists. Use it at the start of a session to discover assets (such as a brand board) created earlier, so you can reuse them or pass them to edit_image as references.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the responsibility for behavioral disclosure. It transparently explains that only images written by the server are listed, that the prompt is included only when a .json sidecar exists, and that the default directory is ./assets/generated. This gives the agent a good understanding of what to expect, though it does not describe edge cases like an empty directory.

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 two sentences with no filler. The first sentence front-loads the core listing behavior and return fields, while the second adds practical session-start guidance. Every clause contributes necessary information.

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 low complexity, the presence of an output schema, and its sibling tools, the description is complete. It covers what is listed, where from, the conditional behavior, and how to use the results, so an agent has everything needed to invoke it correctly.

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 tool has zero parameters and 100% schema coverage, so there is no parameter ambiguity. The description adds context by confirming there are no options and that calling it simply lists all generated assets, which is sufficient.

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 clearly states a specific action and resource: listing images written to the generated-assets directory. It also specifies exactly what data is returned (absolute path, dimensions, size, prompt) and frames the purpose as discovering reusable assets, which separates it from the sibling generation and editing tools.

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?

The description gives explicit guidance: use it at the start of a session to discover previously created assets and to pass them as references to edit_image. It does not explicitly state when not to use it versus generate_image, but the intended workflow is clear enough to select this tool appropriately.

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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