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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is low. The description still adds real context by characterizing this as an aggregate overview rather than a raw listing, and by scoping it to Canton specifically, which helps set expectations about the response.

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?

Three short sentences, front-loaded with what the tool returns, followed by scope and usage. Every sentence carries information and nothing is padded.

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?

With no output schema, the description does the work of describing the return content (counts, top categories, recent activity), and annotations handle the safety profile. The only gap is freshness or granularity of "recent activity," which is minor for a single-parameter read tool.

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 description coverage is 100% and there is only one optional parameter, so the schema already documents the category filter with examples. The description reinforces the filter's purpose (judging a category's funding level) but adds no syntax or format detail beyond the 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 names the resource (Canton Dev Fund) and enumerates the shape of the output: proposal counts by state and label, top categories, recent activity. This distinguishes it from narrower siblings like get_dev_fund_activity or get_dev_fund_lifecycle, though it never names an alternative explicitly.

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?

"Use to gauge whether a category is over- or under-funded before proposing" gives a clear decision context and ties the tool to a workflow stage. It stops short of naming when NOT to use it or which sibling to prefer for raw proposal lists, so it is clear context without exclusions.

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