td_memory_learn

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

Annotations already provide readOnlyHint, idempotentHint, etc. The description adds valuable detail: auto-detects complexity levels and describes output. No contradictions. Additional behavioral context is useful.

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?

Four sentences, front-loaded with purpose. Bullet list of complexity levels is efficient. No wasted words. Excellent conciseness.

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 no output schema, the description explains the return value ('technique dict') and next step. Covers complexity behavior. Could detail what a 'technique recipe' is, but overall sufficient.

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 coverage is 100% with descriptions. The description does not add significant meaning beyond the schema. For 'path' it mentions 'root path of the network subtree' which aligns with schema. No extra clarification.

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 'analyze and extract' and the resource 'reusable technique recipe from a network subtree'. It distinguishes itself from siblings like td_memory_save by noting the output is a dict to be passed to save.

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?

Provides clear guidance: returns a technique dict to be passed to td_memory_save. Also describes auto-detection of complexity for different subtree sizes. However, no explicit when-not-to-use versus alternative tools.

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