tail_log

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

The description discloses token-based pagination to respect AI context limits, which is critical for correct invocation. However, it fails to explain the interaction between the 'lines' and 'max_tokens' parameters, and no annotations are provided to indicate the operation is read-only.

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?

Two concise sentences that front-load the purpose and key behavior. Every sentence carries distinct information with no redundancy.

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?

The description covers core purpose and an important behavior (token pagination). However, it omits error handling, return format, and the exact interplay between 'lines' and 'max_tokens', leaving some gaps for an agent.

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 schema provides full coverage for all three parameters. The description adds value by explaining the token-pagination context for max_tokens and likening the tool to the Unix 'tail' command, which aids understanding of the lines parameter.

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 reads the end of a log file using a Unix 'tail' analogy, and distinguishes itself from siblings like head_log and read_log_paginated by explicitly mentioning token-based pagination for AI context limits.

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 phrase 'Ideal for checking recent log entries' implies when to use, but there is no explicit guidance on when not to use or how to choose among siblings like search_log_file or find_errors.

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