query_logs_custom

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

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses that the tool executes arbitrary backend-native queries and supports three different query syntaxes, and it provides limits for timeframe and line count. However, it does not describe the output/return format, error behavior, or any rate/cost implications of running custom queries.

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 compact and well-structured: one front-loaded purpose sentence followed by a clean Args block. Every sentence adds necessary information without repetition or filler.

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 tool with no output schema and no annotations, the description does not explain the return shape or how the query backend is selected/decided among Loki, Datadog, and CloudWatch Insights. It provides enough for basic invocation but leaves meaningful gaps an agent would benefit from knowing.

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 Args block adds meaningful semantics and ranges for all three parameters: query is a 'Backend-native query string', timeframe_minutes is 'How many minutes of logs to retrieve (1-1440)', and limit is 'Maximum number of log lines (1-500)'. The input schema only provides types, titles, and defaults, so the description substantially enriches the 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 a specific operation: 'Execute a custom log query' and names the supported query syntaxes (LogQL, Datadog syntax, CloudWatch Insights). This distinguishes it from simpler log-fetching tools by emphasizing custom/backend-native queries, though it never explicitly names sibling tools.

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 'custom log query' and the list of query languages imply that this tool is intended for backend-native queries not covered by prebuilt tools, but the description provides no explicit when-to-use or when-not-to-use guidance and names no alternatives. Usage context must be inferred rather than stated.

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