agent_set_preference

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

No annotations exist, so the description fully bears the burden. It discloses authentication (PUBLIC), risk (READ, local file write, no fund risk), and return size (~200B). This provides necessary behavioral context beyond the schema.

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 well-structured with clear sections: function, scenario, keywords, parameters, auth, risk, return size, relationship, and common keys. Every section adds value without verbosity. It is front-loaded with the core purpose.

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 simplicity (2 params, no output schema), the description covers all necessary aspects: purpose, usage scenario, parameter guidance, risk, auth, and relationships to sibling tools. It provides a complete and self-contained understanding.

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 covers both parameters with descriptions. The description adds value by listing common key names and their meanings (e.g., default_instId, risk_level), which helps the agent form correct invocations beyond the schema.

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's purpose: setting persistent preferences for the agent, with cross-session retention. It provides a concrete scenario (user says 'future default trading pair') and lists keywords, making it distinct from sibling tools like agent_get_preference.

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 includes a scenario and keywords for when to use, and explicitly links to agent_get_preference for reading. It provides common key names as guidance. While no explicit 'when not to use' is given, the context is sufficient for correct invocation.

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