post_classification

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

With no annotations, the description carries the full burden. It does disclose the return shape (per-facet distribution of [value, document count, hit count]), which is meaningful. However, it omits read-only/permission behavior, error conditions, and what the facet set actually contains beyond a vague ellipsis.

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?

Purposes are front-loaded, followed by return format and an arg list. The structure is clean and each section pulls weight, with only mild redundancy in the arg explanations.

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?

Because an output schema exists, the description needn't explain return values, yet it helpfully summarizes them. Parameters are all covered. It stops short of permission requirements or sibling routing, but for a facet-distribution tool this is nearly sufficient.

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 coverage is 0%, so the description must carry parameter meaning — and it does, documenting all five: db, the '.all' query sentinel, corpus with '[ALL]', target accepting 'OPEN'/'USER', and owner_username for friend-shared databases. That is strong compensation, though enum-style constraints are only hinted, not formalized.

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 opening sentence gives a specific verb and resource — breaking a query's hits down by post-classification facets — and goes further by naming the facets (corpus, period, place, category). It distinguishes itself from sibling retrieval tools like search_documents or tag_analysis by framing the output as a distribution rather than a hit list.

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 phrase 'the quickest way to see how a term is spread across a database' implies when the tool is valuable, but no alternative tool is named and no explicit when-not/exclusion is given. Usage is implied rather than stated.

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