Phenomenai

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 effectively explains key behaviors: the tool submits proposals that go through automated review (structural validation, deduplication, quality scoring) before being added to the dictionary. However, it lacks details on permissions, rate limits, or error handling, which would be helpful 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 well-structured and front-loaded: the first sentence states the purpose, followed by context on the submission process, then a clear parameter breakdown. Every sentence earns its place by providing essential information without redundancy, making it efficient and easy to parse.

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 mutation tool with no annotations, 7 parameters, and an output schema (which reduces the need to describe return values), the description is largely complete. It covers purpose, process, and parameter details thoroughly. However, it could improve by mentioning authentication needs or response formats, given the tool's complexity.

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?

Given 0% schema description coverage, the description compensates fully by providing detailed semantics for all 7 parameters. It explains each parameter's purpose, format constraints (e.g., character limits for 'term' and 'definition'), optionality, and examples (e.g., 'Context Amnesia' for 'term'), adding significant value beyond the bare schema.

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 with a specific verb ('Propose') and resource ('new term for the AI Dictionary'), and it distinguishes this from siblings like 'lookup_term', 'search_dictionary', or 'propose_terms_batch' by focusing on single-term submission with automated review. The first sentence directly answers 'what does this tool do?'

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 for when to use this tool ('Submit a term describing an AI phenomenology experience'), but it does not explicitly state when NOT to use it or name alternatives. For example, it doesn't contrast with 'propose_terms_batch' for bulk submissions or 'revise_proposal' for updates, leaving some ambiguity about sibling differentiation.

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