log_tool_execution

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. 'Log tool execution for learning' implies a write operation (logging) but doesn't specify whether this is safe, reversible, or has side effects. It lacks details on permissions, rate limits, or what 'learning' entails (e.g., storage, analysis). This is inadequate for a tool with potential mutation implications.

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 very concise with a single phrase, 'Log tool execution for learning', which is front-loaded and wastes no words. However, it's arguably too brief, bordering on under-specification, but within the scope of conciseness, it's efficient if not fully informative.

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 complexity (3 parameters with nested objects, no output schema, and no annotations), the description is incomplete. It doesn't explain the tool's role in the learning system, what happens after logging, or how to interpret inputs. For a tool that likely involves data mutation and integration with other learning tools, more context is needed to guide effective use.

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?

Schema description coverage is 0%, so the schema provides no parameter details. The description mentions 'tool execution' which hints at 'tool_name', 'args', and 'result', but doesn't explain their meanings, formats, or constraints. For example, it doesn't clarify what 'args' and 'result' should contain or how they relate to learning. This adds minimal value beyond the parameter names.

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 'Log tool execution for learning' states a general purpose (logging for learning) but is vague about what specifically gets logged and how it differs from other logging or learning tools. It doesn't clearly distinguish from sibling tools like 'get_learned_preferences' or 'list_recent', which might also involve learning-related operations. The phrase 'tool execution' is somewhat specific but lacks detail about scope or mechanism.

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. It doesn't mention prerequisites, timing (e.g., after tool use), or exclusions. With siblings like 'get_learned_preferences' and 'list_recent' that might overlap in learning contexts, there's no differentiation, leaving the agent to guess based on names alone.

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