filter_describe

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

No annotations are provided, so the description carries the full behavioral burden. 'Describe' strongly implies a safe read operation and the existence of an output schema covers return values, but the description never confirms it is read-only (no side effects on GEGL state) or mentions any constraints. It is adequate but thin for a zero-annotation 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?

A single front-loaded sentence with no filler. The purpose and the downstream benefit are both stated compactly.

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?

For a simple one-parameter describe tool with an output schema, the description covers what it does and why it is useful, which is nearly sufficient. Minor gaps: no note on the operation-name format or a pointer to filter_list for valid names.

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 0% and the single 'operation' parameter has no description in the schema. The description only obliquely indicates the parameter is a GEGL operation, but gives no expected format or example (e.g. 'gegl:blur'), so it fails to compensate for the coverage gap.

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?

States a specific verb ('describe') and resource ('a GEGL operation's configurable properties'), and even names the downstream goal (constructing a valid filter_apply call). An agent can clearly distinguish it from siblings filter_list and filter_apply without opening the schema.

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 phrase 'so an AI can construct a valid filter_apply call' implies this is a prerequisite step before filter_apply, which is useful implied guidance. However, it never explicitly states when to call this vs filter_list (to discover operation names) or filter_apply, nor any ordering or exclusion, leaving the workflow inference-based.

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