list_loaded_images
Lists Mach-O images loaded in the approved target process for memory inspection and analysis.
Instructions
List Mach-O images loaded in the approved target.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Lists Mach-O images loaded in the approved target process for memory inspection and analysis.
List Mach-O images loaded in the approved target.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already indicate a read-only, idempotent, non-destructive operation, so the safety profile is covered. The description adds the 'approved target' scope but does not disclose behavior like the output format or that it returns base addresses, paths, or other details. This is moderate additional context beyond annotations.
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 a single, concise sentence that immediately conveys the action and scope. Every word adds value, with no filler or repetition. It is appropriately front-loaded for quick agent parsing.
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?
For a zero-parameter read-only listing tool, the description captures the essential purpose and scope. However, it does not mention what the returned list contains (e.g., paths, addresses, UUIDs), which could matter for an agent deciding whether this tool solves a task. Given the lack of an output schema, a bit more return detail would improve completeness.
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 tool has zero parameters and an empty input schema, so there are no parameter semantics to explain. The baseline of 4 for zero-param tools applies, and the description does not need to compensate for any parameter documentation gaps.
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 action (List) and the resource (Mach-O images loaded in the approved target). This distinguishes it from sibling tools like list_memory_regions, which target a different subject. The phrasing is specific and unambiguous.
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 gives no guidance on when to use this tool versus alternatives. It does not mention cases where list_memory_regions or other enumeration tools would be more appropriate, nor any exclusions or prerequisites. The agent is left without contextual selection help.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/krisgnyc/bitslicer-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server