convert_lql_to_dql

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

No annotations are provided, so the description must disclose behavioral traits. It notes the tool is 'safe, read-only', which is good, but lacks details on error handling, performance, or prerequisites. More information would be helpful but the core safety is stated.

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 two sentences, front-loaded with the action and resource. Every phrase is meaningful, with no unnecessary words. It is concise and well-structured.

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?

No output schema exists, yet the description does not explain what the tool returns (e.g., the converted DQL string). It also omits details on error conditions or limitations. For a conversion tool, this information is important and missing.

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 100%, so the baseline is 3. The description does not add extra information beyond the schema for the parameters; the schema itself explains the query parameter with an example. Thus no additional value is provided.

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 ('Convert a legacy LQL matcher expression to a DQL equivalent') and the resource (LQL to DQL). It distinguishes from sibling tools as no other tool performs this conversion, and provides specific context ('migrating older pipeline routing conditions').

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?

Explicitly states when to use ('when migrating older pipeline routing conditions'). It does not list when not to use or provide alternatives, but the context is clear and sufficient for an AI agent to decide.

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