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

No annotations are provided, so the description carries the full burden. It fully discloses the output format (flat per-class rows with counts, percent, mean_confidence; per-neuron rows if aggregate=false), the ability to split by dataset, the GO secretion term id space, and the effect of exclude_dbs. This is exemplary transparency for a query tool with no side effects.

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 long but efficiently structured: purpose first, then when-to-use, then output variants, then constraints and recommendations. Every sentence adds distinct value with no fluff. The use of section markers (CONSTRAINTS, RECOMMENDED) improves scannability for an agent.

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 tool with 5 parameters, no output schema, and no annotations, the description provides all necessary information: output formats, parameter effects, id-space alignment with a sibling tool, constraints, and recommended defaults. An agent can confidently invoke it correctly without additional lookups.

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?

Although schema coverage is 100%, the description adds meaning beyond the schema: it explains why aggregate=false returns per-neuron rows, why split_by_dataset=true reveals cross-connectome agreement, how min_confidence filters predictions, and the rationale for the default exclude_dbs. This contextualizes each parameter's purpose, exceeding the schema's bare 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 states a specific verb ('Get'), a precise resource ('PREDICTED neurotransmitter(s) for a Drosophila neuron class — itself or any subclass'), and the data source ('per-instance connectome predictions'). It explicitly differentiates from its sibling get_known_neurotransmitters by contrasting data-driven predictions with ontology-curated classifications.

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 explicit usage context: 'Use this for "what neurotransmitter does <cell type> use?" when you want the data-driven prediction and its confidence.' It also names the alternative (get_known_neurotransmitters) and explains the distinction. Constraints (neuron class terms only, canonicalization via search_terms) and a recommended default (exclude_dbs) further guide correct invocation.

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