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play_and_capture

Automate Pyxel game testing by simulating keyboard/mouse input at specific frames and capturing screenshots to verify input-dependent logic without manual play.

Instructions

Play a game by sending simulated input and capture screenshots.

Simulates keyboard/mouse input at specific frames and captures screenshots at specified frame points. Use this to test input-dependent game logic (menus, movement, shooting) without manual play.

Args: script_path: Absolute path to the .py script to run. inputs: JSON array of input events. Each event: {"frame": N, "keys": ["KEY_SPACE", ...], "mouse_x": X, "mouse_y": Y} Keys are held from their frame until a later entry changes them. Default state: no keys pressed, mouse at (0,0). frames: Comma-separated frame numbers to capture screenshots (default: "1,30,60"). scale: Screenshot scale multiplier (default: 1). timeout: Maximum seconds to wait for the script (default: 30).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
script_pathYes
inputsYes
framesNo1,30,60
scaleNo
timeoutNo
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes key behaviors like simulating keyboard/mouse input, capturing screenshots at frames, and default states, but lacks details on permissions, rate limits, error handling, or what happens after timeout. It adds value but is incomplete for a mutation tool.

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

Conciseness4/5

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

The description is well-structured with a purpose statement, usage context, and a clear 'Args' section. Every sentence earns its place, but it could be slightly more concise by integrating the default explanations into the parameter list more tightly. It is front-loaded with the core functionality.

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

Completeness3/5

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

Given the complexity (5 parameters, no annotations, no output schema), the description is moderately complete. It covers parameters well but lacks output details (e.g., what screenshots look like), error scenarios, or integration with sibling tools. For a tool with simulation and capture, more behavioral context would be beneficial.

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?

Schema description coverage is 0%, so the description must compensate fully. It provides detailed semantics for all 5 parameters, including format examples for 'inputs' (JSON array with frame, keys, mouse coordinates), default values for 'frames', 'scale', and 'timeout', and explanations of key holding behavior. This adds significant meaning beyond the bare 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 clearly states the tool's purpose with specific verbs ('play a game by sending simulated input and capture screenshots') and distinguishes it from siblings like 'capture_frames' or 'run_and_capture' by emphasizing input simulation for testing game logic. It explicitly mentions testing menus, movement, and shooting without manual play.

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 clear context for when to use this tool ('to test input-dependent game logic without manual play'), but it does not explicitly state when not to use it or name specific alternatives among the sibling tools. The guidance is helpful but lacks explicit exclusions or comparisons.

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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