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fuzzmind

fuzzmind-frida-mcp

by fuzzmind

frida_native_function_call

Invoke a native function at a specified address in a target process, with configurable return type and arguments.

Instructions

Call a native function by address inside a target process.

Constructs a NativeFunction with the given signature and invokes it.

target: process name or pid (string). address: hex address (e.g. '0x100004000'). return_type: return type ('void', 'int', 'pointer', 'uint64', etc.). arg_types: list of argument types (e.g. ['pointer', 'int']). args: list of argument values as strings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetYes
addressYes
return_typeYes
arg_typesYes
argsYes
Behavior2/5

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

With no annotations, the description carries full burden. It mentions constructing and invoking a NativeFunction but fails to disclose critical behavioral traits: whether the call is synchronous, how errors are handled, side effects, or that the process must be attached. Gaps exist.

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 front-loaded with a clear summary sentence, followed by a concise bullet list of parameters. Each bullet is one line with no wasted words. Efficient and readable.

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 5 required parameters and no output schema, the description covers parameter semantics adequately. However, it lacks information about return values, error handling, and process attachment requirements. The tool is moderately complex but the description is minimally adequate.

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 description coverage is 0%, but the description adds meaning beyond schema: it explains each parameter (e.g., address is a hex string, return_type example values). It provides examples and clarifies required types. However, it does not specify valid enum values for return_type or constraints on arg_types/args correspondence.

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 calls a native function by address, specifying it constructs a NativeFunction with given signature and invokes it. This distinguishes it from siblings like frida_hook_native_function (hook) or frida_system_function_call (system calls).

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

Usage Guidelines3/5

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

The description lists parameters but does not explicitly state when to use this tool versus alternatives (e.g., when to use raw address vs symbol name). No usage context or prerequisites are provided.

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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