Skip to main content
Glama

screenshot_workspace

Capture a snapshot of the ComfyUI canvas to visually inspect workflow layout, node overlap, and link positions. Choose framing for whole graph, viewport, or selected nodes.

Instructions

Photograph the ComfyUI canvas: what the workflow looks like, not what it says.

This answers the questions a graph dump cannot - whether the layout reads as a mess, which boxes overlap, where a link crosses the whole screen, what the user means by "that one over there". After a layout change it is the only way to check the result rather than assume it.

It is a picture of the canvas, and that has a hard edge: prompts, image previews, markdown notes and audio players are HTML drawn over the canvas and are not in it. An empty-looking prompt box in the picture says nothing about the prompt - get_workspace_graph is what reads values. The report says how many such widgets the graph has.

Always the graph on screen. To photograph a subgraph, navigate_workspace into it first; the report names which graph it is.

Args: fit: what to frame. "graph" for the whole workflow, "view" for the viewport exactly as the user has it, "selected" for what they have clicked on, or a list of node ids to frame those. A whole large graph is legible only as a shape - for reading titles, frame a handful of nodes. max_edge: longest edge in pixels, 256 to 4096. Bigger reads better and costs more; it does not make a wide graph legible, only a small subset. format: "png" keeps the text crisp and is the right answer for a diagram. "jpeg" and "webp" are smaller and blur it. client_id: which tab to ask; defaults to the most recently focused one.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fitNograph
formatNopng
max_edgeNo
client_idNo
Behavior5/5

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

With no annotations provided, the description fully carries the transparency burden. It discloses a critical limitation: HTML overlays (prompts, previews, notes) are not captured, and states that an empty-looking prompt box does not reflect the prompt. It also explains that max_edge affects legibility and cost, and that the default client_id is the most recently focused tab.

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 well-structured with a clear introduction, explanation of limitations, and an Args section. Every sentence earns its place, providing necessary context without fluff. It is front-loaded with the core purpose and then expands into actionable details.

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 has no output schema, the description adequately sets expectations about outputs (a picture of the canvas, plus a report mentioning widget count and graph name). It also covers all parameters and scenarios (entire graph, subgraph, viewport, selected nodes) making it sufficiently complete for an agent to invoke correctly.

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

Parameters5/5

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

The input schema has 0% description coverage, but the description compensates fully. Each parameter is explained with concrete meanings, examples, and usage guidance: fit options and their visual implications, max_edge range and trade-offs, format rationale for crispness vs. size, and client_id default behavior. This adds substantial value beyond the raw schema.

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 opens with a clear verb and resource: 'Photograph the ComfyUI canvas' and immediately distinguishes its purpose from graph dumps by highlighting what visual information it captures (overlaps, crossing links, layout mess). This directly addresses the tool's role among siblings like get_workspace_graph.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool: 'After a layout change it is the only way to check the result rather than assume it.' It also names alternatives and exclusions: get_workspace_graph for reading values, navigate_workspace to frame subgraphs. This gives clear context for choosing between tools.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/pytraveler/local-comfyui-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server