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0xhackerfren

Frida Game Hacking MCP

by 0xhackerfren

read_registers

Retrieve CPU register values for game debugging and reverse engineering. This tool provides thread and architecture information to analyze program execution states.

Instructions

Read CPU register values.

Note: Full register context available in hook callbacks via 'this.context'.

Returns:
    Basic thread and architecture info.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler function for the 'read_registers' tool. It injects a Frida script to retrieve basic process architecture, platform, pointer size, and current thread ID. Notes that full register access is available within hook callbacks using 'this.context'.
    @mcp.tool()
    def read_registers() -> Dict[str, Any]:
        """
        Read CPU register values.
        
        Note: Full register context available in hook callbacks via 'this.context'.
        
        Returns:
            Basic thread and architecture info.
        """
        global _session
        
        if not _session.is_attached():
            return {"error": "Not attached. Use attach() first."}
        
        try:
            script_code = """
            send(JSON.stringify({
                arch: Process.arch,
                platform: Process.platform,
                pointer_size: Process.pointerSize,
                thread_id: Process.getCurrentThreadId()
            }));
            """
            
            result_data = []
            def on_message(message, data):
                if message['type'] == 'send':
                    result_data.append(message['payload'])
            
            script = _session.session.create_script(script_code)
            script.on('message', on_message)
            script.load()
            script.unload()
            
            import json
            result = json.loads(result_data[0]) if result_data else {}
            result["note"] = "Full registers available in hook via 'this.context'"
            return result
        
        except Exception as e:
            return {"error": f"Failed to read registers: {str(e)}"}
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 states the tool reads values (implying a read-only operation) and describes the return format as 'Basic thread and architecture info,' which adds useful context beyond the basic purpose. However, it doesn't cover potential limitations like permissions needed, error conditions, or performance implications.

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 appropriately sized with three sentences that each serve a purpose: stating the tool's function, providing a technical note, and describing returns. It's front-loaded with the core purpose. However, the note about 'hook callbacks' might be slightly tangential for some users, slightly reducing efficiency.

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?

Given the tool has 0 parameters, 100% schema coverage, and an output schema exists, the description is reasonably complete. It explains what the tool does and what it returns, which complements the structured data. For a simple read operation with no inputs, this provides adequate context, though it could benefit from more behavioral details like error handling.

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 0 parameters with 100% schema description coverage, so the schema fully documents the inputs. The description doesn't need to add parameter information, and it doesn't contradict the schema. The baseline for this scenario is 4, as the description appropriately focuses on other aspects without redundant parameter details.

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's purpose as 'Read CPU register values,' which is a specific verb+resource combination. However, it doesn't distinguish itself from potential sibling tools like 'get_session_info' or 'list_capabilities' that might also provide system information, so it doesn't fully differentiate from alternatives.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It mentions that 'Full register context available in hook callbacks via 'this.context',' but this is a technical note rather than usage guidance. There's no explicit when/when-not or alternative tool recommendations.

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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