Skip to main content
Glama

capture_screen

Capture the client's screen from Garry's Mod and return a viewable image. Adjust scale and quality for speed, or capture a specific region for efficiency.

Instructions

Captures the client's screen on the next frame and returns it as a viewable image. Every byte travels in 7KB chunks paced by frame, so a full-resolution capture takes seconds and dominates any act-then-look loop: the default half scale at quality 60 is 4-6x cheaper and still legible for a Derma layout. Pass region (from read_panels' screen_x/screen_y) to capture just one panel. A capture that would exceed the client's channel budget is refused with its size rather than sent: a full-screen quality-80 capture measured 424KB and timed the client out of the server. Requires an active GMod client.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scaleNoDownscale factor applied on the client before encoding. Use 1 to read small text.
regionNoScreen region to capture. Free: render.Capture takes it natively.
qualityNoJPEG quality.
Behavior5/5

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

With no annotations beyond title, the description fully discloses the tool's behavior: data chunking, time cost, budget limits, failure mode (refusal with size), and requirement for an active GMod client. It is highly transparent.

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 concise for the complexity, with information front-loaded and every sentence contributing essential details: purpose, performance, usage tips, requirements. No wasted words.

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

Completeness4/5

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

The description covers purpose, performance, usage, and failure modes. It does not detail the output format beyond 'viewable image', but that is sufficient given the lack of output schema. Overall it provides complete context for an AI agent to use the tool 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?

Schema coverage is 100%, so baseline is 3. The description adds value by recommending parameter values (default scale and quality) and explaining performance implications, and by providing guidance on deriving region from read_panels, enhancing schema documentation.

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 the tool captures the client's screen as an image. It mentions a specific use case (capturing a panel via region from read_panels) but does not explicitly distinguish from sibling tools like read_panels or inspect_entity, which serve different purposes.

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 provides actionable usage guidance: recommends default half scale and quality 60 for efficiency, suggests using region to capture a single panel, and warns about the channel budget and timeout. However, it does not explicitly state when to use this tool over siblings.

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/TheRolists/gmod-mcp'

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