Frida MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool 'returns call logs with arguments and return values', which gives some insight into output behavior. However, it lacks critical details such as whether this is a read-only or destructive operation, performance implications, rate limits, or error handling. For a tool with six parameters and no annotations, this is a significant gap in transparency.

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

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

The description is extremely concise and front-loaded, consisting of only two sentences that directly state the tool's purpose and return value. Every word earns its place, with no redundant or vague language. This makes it easy for an AI agent to quickly grasp the core functionality.

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

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

Given the tool's complexity (six parameters, no annotations, no output schema, and many sibling tools), the description is incomplete. It lacks usage guidelines, behavioral details, and output specifications. While concise, it does not provide enough context for an AI agent to fully understand how and when to use this tool effectively in the broader Frida toolset.

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

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

The description does not add any parameter-specific information beyond what the input schema provides. With a schema description coverage of 67%, the schema documents most parameters well (e.g., 'target', 'include', 'duration'), but parameters like 'device_id' and 'device_type' have limited or no descriptions in the schema. The description does not compensate for these gaps, so it meets the baseline of 3 where the schema does the heavy lifting but doesn't enhance parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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 a specific verb ('trace') and resource ('function calls matching a pattern'), and mentions the return value ('call logs with arguments and return values'). However, it does not explicitly differentiate from sibling tools like 'frida_stalker_trace' or 'frida_hook_function', which may have overlapping or related functionality in the Frida ecosystem.

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

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. With many sibling tools available (e.g., 'frida_stalker_trace', 'frida_hook_function'), there is no indication of specific use cases, prerequisites, or exclusions. This lack of context makes it difficult for an AI agent to choose appropriately among similar tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.