knowledge_update

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

With no annotations provided, the description fully discloses critical behaviors: synchronous return for minor edits, asynchronous queuing with polling requirement for significant content changes, client-side JSON validation, and expected latency (~15-30s). It also specifies the async return shape and directs to get_job. This is exceptionally transparent about side effects and API behavior.

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 three sentences long and front-loaded with the primary purpose. Every sentence adds critical information: the update action, the sync/async dichotomy, JSON validation, and timing. No unnecessary words or repetition.

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 adequately explains return values: the updated item for minor edits, and { operationId, status } for significant changes, with polling instructions referencing get_job. It also covers validation and timing. For a tool with 7 parameters and complex async behavior, the description is sufficiently complete to guide an agent without further documentation.

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%, so the baseline is 3. The description adds value by mapping parameter categories to behavioral outcomes: name/tags/description are minor edits (synchronous), while content changes trigger AI regeneration (asynchronous). This tells the agent which parameters to prioritize and what consequences to expect, going beyond the schema's field-level descriptions.

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 ('Update a knowledge-base item') with a specific verb and resource. It distinguishes itself from sibling tools (create/get/list/delete) and goes further by describing the two modes of update (synchronous minor edits vs asynchronous significant content changes), which uniquely characterizes this tool.

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 provides clear context on when this tool is appropriate: updating an existing knowledge-base item. It distinguishes between minor and significant content changes, guiding the agent on expected response behavior. However, it does not explicitly mention alternatives (e.g., knowledge_create for new items) or conditions when not to use it, so it misses the full 'when/when-not' guidance.

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