add_learning

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

No annotations exist, so description carries full burden. Discloses critical behaviors: content is auto-processed (entity/relationship extraction) and coherence warnings are surfaced. Does not mention error handling or permissions, but core behavioral traits are covered.

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?

Three sentences front-loaded with main purpose, each sentence adds value. Could be slightly more structured with parameter details, but overall efficient and clear.

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?

With no annotations, no output schema, and nested parameters, description is somewhat lightweight. Covers core purpose and behavioral traits, but fails to explain return behavior or parameter usage fully. Adequate for a simple creation tool but leaves gaps.

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 0%, so description must compensate but fails to. Does not explain the meaning of 'scope' (nested object with plan_id, goal_id, node_id), 'entry_type' enum values, or 'source_description'. Only 'content' is implied but not clarified.

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?

Description starts with 'Record a knowledge episode,' clearly stating the main action. Lists specific use cases (after research, on decisions, discovering context) which differentiates it from sibling tools like recall_knowledge (retrieval) and others.

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 says when to use: after research, on decisions, or when discovering important context. Also mentions automatic entity extraction and coherence warnings, helping the agent understand context. Lacks explicit when-not-to-use or alternative tool names, but sufficient given the clear purpose.

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