AICTIONBOT Werkbank

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

The description adds meaningful caveats beyond the readOnlyHint and openWorldHint annotations: deterministic processing, no AI, no personal data, and not legal advice. It also explains the comparison method and returned verdict categories, though it does not discuss data freshness, index publication lag, or missing-category behavior.

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 purpose is front-loaded and the description is dense with useful details rather than filler. The branding line is minor overhead, but the remaining sentences earn their place by explaining method, outputs, and caveats.

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 usefully summarizes what is returned: market change, gap, verdict, counter-offer level, and source. It also covers scope and limitations such as Germany-only and not legal advice, leaving only minor operational gaps around data availability and recency.

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%, so all five parameters are already documented in the schema. The description reinforces the category prerequisite via list_categories and explains that goods versus services categories affect which index is used, but it does not add substantial meaning beyond the schema for last_agreement, annual_volume_eur, or language.

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 and resource: comparing a supplier's requested price increase against official Eurostat market data for Germany. It clearly distinguishes the tool from the only sibling, list_categories, which is positioned as a prerequisite rather than an alternative.

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?

It gives a clear usage context, including the Germany scope and the instruction to use list_categories to pick a category code. It does not explicitly state when not to use the tool, but with only one sibling that serves a preparatory role, the guidance is sufficient.

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

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

Annotations already declare readOnlyHint=true, so the safety profile is covered by structured data. The description adds genuinely useful context beyond that: the taxonomy (Eurostat NACE), geographic scope (Germany), and that records carry both German and English names. It does not say whether the list is static or versioned, or how records are ordered.

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?

One sentence, front-loaded with the verb and resource, with the qualifying details (taxonomy, region, bilingual names) packed into a compact clause. Nothing is redundant with the title and nothing could be cut without losing information.

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 single-parameter read-only enumeration with no output schema, the description gives enough to call it correctly and understand what comes back. The one remaining gap is the effect and default of the optional `language` parameter, which is left to the agent to guess.

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 single `language` parameter has a two-value enum but 0% schema description coverage, so the description must carry the semantics. Mentioning "German and English names" hints at the bilingual nature of the data but never states whether `language` selects the output language, filters records, or is simply optional with a default (required=false is unexplained).

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?

A specific verb ("Lists") plus a specific resource ("categories the AICTIONBOT™ price increase check covers"), with the underlying data source named (Eurostat NACE codes for Germany) and the return contents previewed (German and English names). This is clearly distinct from the only sibling, check_price_increase, which performs the check rather than enumerating the valid categories.

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 categories the ... price increase check covers" implies this is the discovery/lookup companion to check_price_increase, so usage is inferable. But there is no explicit instruction such as "call this first to obtain valid category codes before calling check_price_increase," and no when-not guidance.

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