constraint_list

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

Annotations already declare readOnlyHint=true, indicating a safe read operation. The description adds value by specifying the return structure (dictionary with 'constraints' and 'usage' keys) and metadata details, but does not disclose additional behavioral traits like rate limits, auth needs, or error handling beyond what annotations provide.

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 front-loaded with the core purpose in the first sentence, followed by a structured explanation of returns. Every sentence adds value without waste, making it efficient and well-organized for quick understanding.

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 tool's complexity (simple list operation), annotations (readOnlyHint), and the presence of an output schema (implied by the detailed return description), the description is complete. It covers purpose, usage context, and output details without redundancy, suiting the tool's needs.

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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing on output semantics instead, which aligns with the baseline for zero parameters.

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 verb ('Return') and resource ('list of all registered LMQL constraints with their metadata'), making the purpose specific. It distinguishes from sibling tools like constraint_validate (which validates constraints) and data-related tools, avoiding tautology by not just restating the name.

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 implies usage by mentioning how to validate constraints (e.g., use constraint_validate), providing clear context for when this tool is relevant. However, it does not explicitly state when to use this tool versus alternatives like data_query or when not to use it, missing explicit exclusions.

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