Skip to main content
Glama
fuzzmind

fuzzmind-frida-mcp

by fuzzmind

frida_spoof_return

Spoof function return values to bypass anti-debug checks. Attaches Interceptor.onLeave to replace return values of functions like ptrace, sysctl, or isDebuggerAttached on each call.

Instructions

Spoof the return value of a function. Classic anti-debug bypass.

Attaches Interceptor.onLeave and replaces the return value on every call. Common uses: bypass ptrace anti-debug, spoof isDebuggerAttached, override capability checks.

target: process name or pid (string). function_name_or_addr: hex address or exported symbol name (e.g. 'ptrace', 'sysctl', 'isDebuggerAttached'). return_value: value to force as return (numeric string, e.g. '0', '1', '0xffffffff'). Interpreted via ptr().

Stays active for 30 seconds. Returns confirmation and logs the first 50 spoofed calls with original vs. spoofed values.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetYes
function_name_or_addrYes
return_valueYes
Behavior5/5

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

Without annotations, the description fully discloses the mechanism (Interceptor.onLeave), duration (30 seconds), and logging behavior (first 50 calls), which is transparent and beyond what annotations would provide.

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 purpose, mechanism, parameters, and additional info in a clear flow, though slightly verbose in listing examples.

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 no output schema, the description covers return values (confirmation and logs) and all necessary context for the tool's operation, making it complete for an agent to use 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?

Schema coverage is 0%, but the description explains each parameter with examples and interpretation (ptr()), adding significant meaning beyond the schema's type-only definitions.

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 'Spoof the return value of a function' and provides a classic anti-debug bypass context, distinguishing it from siblings like frida_anti_debug_bypass which may have broader scope.

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?

It gives explicit use cases (bypass ptrace, spoof isDebuggerAttached) and function examples, but does not compare to similar tools like frida_anti_debug_bypass for exclusion cases.

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/fuzzmind/frida-mcp'

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