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dryfryce

Frida MCP Server

by dryfryce

frida_java_call_method

Execute Java methods on Android applications during runtime to test functionality, debug issues, or modify behavior without altering source code.

Instructions

Call a method on a Java class or instance (Android).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
session_idYes
class_nameYes
method_nameYes
argsNoMethod arguments
staticNo
Behavior1/5

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 but offers minimal information. It doesn't describe whether the call is synchronous/asynchronous, error handling, performance impact, security implications, or what happens if the method doesn't exist. For a tool that executes code in a live Android environment, this lack of transparency is critical and inadequate.

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 a single, efficient sentence that states the core purpose without fluff. It's front-loaded with the essential action and target, and the parenthetical '(Android)' adds necessary context concisely. Every word earns its place, making it optimally brief for the information provided.

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

Completeness2/5

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

Given the complexity (executing Java methods in Android via Frida), lack of annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't address behavioral risks, return values, error cases, or dependencies on other tools (e.g., 'frida_attach' for sessions). For a tool with potential side effects in a dynamic analysis context, this is a significant gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is low (20%), with only 'args' having a description ('Method arguments'). The description adds no parameter semantics beyond what the schema provides—it doesn't explain 'session_id' (Frida session identifier), 'class_name' format (e.g., fully qualified), 'method_name' specifics, or 'static' implications. With 5 parameters and poor schema coverage, the description fails to compensate, leaving most parameters undocumented.

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 action ('Call a method') and target ('on a Java class or instance'), with the parenthetical '(Android)' providing platform context. It distinguishes from siblings like 'frida_java_hook_method' (which hooks rather than calls) and 'frida_objc_call_method' (which targets Objective-C). However, it doesn't specify the verb's scope (e.g., synchronous execution, return value handling), making it slightly less specific than a perfect 5.

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 doesn't mention prerequisites (e.g., needing an active Frida session), compare it to 'frida_evaluate' (for JavaScript) or 'frida_rpc_call' (for RPC), or specify use cases like testing or debugging. The agent must infer usage from the tool name and context alone.

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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