update_entity

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds some context: it mentions that successful execution returns a link to the updated entity, which is useful for understanding output behavior. However, it does not cover critical aspects like authentication requirements, error handling, rate limits, or whether the update is reversible, leaving gaps in transparency for a mutation tool.

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 appropriately sized and front-loaded, starting with the core purpose followed by examples and additional notes. The examples are detailed but necessary for clarity, and the text avoids redundancy. However, the warning about non-existent databases could be more concise, slightly affecting efficiency.

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 complexity of a mutation tool with no annotations and no output schema, the description is moderately complete. It covers the basic operation, parameter examples, and output behavior (returning a link), but lacks details on error cases, permissions, or side effects, which are important for full contextual 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 has 100% description coverage, so the baseline is 3. The description adds value by providing concrete examples that clarify parameter usage, such as the format for entity fields and special handling for document fields with 'append' and 'content'. This enhances understanding beyond the schema, justifying a higher score.

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: 'Update Fibery entity with specified fields.' It specifies the verb ('Update'), resource ('Fibery entity'), and scope ('with specified fields'), making it easy to understand what the tool does. However, it does not explicitly differentiate from sibling tools like 'create_entity' or 'create_entities_batch', which would require a more specific comparison.

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 implied usage guidance through examples, such as noting to use databases from the user's schema and showing how to handle document fields. However, it lacks explicit when-to-use instructions compared to alternatives like 'create_entity' or 'query_database', and does not mention prerequisites or exclusions, leaving some ambiguity for the agent.

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