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

No annotations are provided, so the description carries the full burden. The 'list' verb correctly implies a read-only operation, and the description adds context about what the output contains (labels and symbols) and how it relates to exclude_dbs. It could have disclosed more about the response structure, but for a parameterless list tool this is adequate.

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 three sentences, front-loaded with the purpose, followed by usage guidance and an example of common datasets. Every sentence adds value with no repetition or fluff.

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 zero-parameter listing tool with no output schema, the description is complete. It states what the tool does, what it returns, how to use the results, and when to call it. The mention of common dataset symbols adds useful context.

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 zero parameters, so the baseline is 4. The description doesn't need to explain parameters, but it does mention that symbols are used in exclude_dbs arguments, which adds semantic context about the use of the tool's output.

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 'List available connectome datasets with their labels and symbols', which is a specific verb (list) and resource (connectome datasets). This distinguishes it from sibling tools like query_connectivity, resolve_entity, and search_terms, which perform different operations.

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 guidance: 'Use the returned symbols when constructing exclude_dbs arguments for query_connectivity' and 'Call this tool if unsure which dataset symbols are valid.' This tells the agent exactly when and how to use the tool relative to its sibling query_connectivity.

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