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

With no annotations provided, the description takes on full responsibility for behavioral disclosure. It explains that results come from ontology classification rather than per-instance predictions, explicitly states there is no confidence value, describes the exact output row shape, and covers the empty-result behavior. This is comprehensive transparency for a simple lookup 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 dense with useful information but not verbose; every sentence serves a purpose. It is front-loaded with the tool's core purpose, then proceeds logically to usage, output format, edge-case behavior, and constraints. There is no filler or redundancy.

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?

Even without an output schema or annotations, the description fully explains what the tool returns, the shape of each row, the meaning of the neurotransmitter id space, and what happens in the empty case. Combined with the parameter documentation, it gives an agent everything needed to call the tool correctly and interpret its response.

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 schema coverage is 100%, so the schema already documents the single parameter well, including examples and meaning of subclasses. The description adds extra value by clarifying that only neuron class terms are accepted and by recommending use of search_terms with specific filter_types for canonicalization. This goes beyond the schema's literal parameter definition.

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 a specific verb and resource: getting curated/known neurotransmitters for a Drosophila neuron class and its subclasses. It explicitly distinguishes itself from per-instance predictions and from the sibling get_predicted_neurotransmitters. An agent can immediately understand what this tool does and how it differs from similar tools.

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 gives explicit when-to-use guidance: use it for curated/established answers to 'what neurotransmitter is <cell type> known to use?' It even tells the agent what to do when the result is empty, directing it to get_predicted_neurotransmitters. It also provides concrete constraints and canonicalization advice via search_terms, leaving no ambiguity about the intended workflow.

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