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

Goes well beyond the annotations (which already declare readOnly/idempotent/closed-world) by disclosing data provenance (RansomLook inventory, CIRCL, CC BY 4.0), the included methodology ('frag.hugo monthly assessment and method'), and a crucial epistemic caveat that counts are perpetrator claims rather than confirmed incidents. That caveat materially changes how an agent should present the numbers, which annotations alone would never convey.

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?

Front-loaded with the core scope in the first sentence, followed by provenance and exclusion in two short follow-ups — no filler. The first sentence is long and carries jargon ('frag.hugo monthly assessment and method') that an uninitiated agent may parse awkwardly, which keeps it from a 5.

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 read-only, zero-required-parameter aggregation tool with a full output schema, the description supplies everything an agent needs: scope, time windows, breakdown dimensions, source, licence, caveat about unconfirmed claims, and a negative scope. Return-value details are rightly left to the output schema.

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 branche, sprache and zeitraum are already fully documented in the schema, and the description only implicitly echoes them ('by industry and group', 'last 30 days or 12 months'). It adds no syntax, matching behaviour for the free-text industry field, or language-default nuance beyond what the schema states, so the baseline 3 applies.

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?

States a specific verb and resource: it summarises counts of German organisations named by ransomware groups on leak sites, broken down by industry and group, over 30 days or 12 months. The closing exclusion ('Not for looking up individual victims') plus the named data source (RansomLook) make it easy to distinguish from both the sibling GDPR tools and a victim-lookup tool.

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?

Usage context is clear: aggregate situational picture, with an explicit when-not ('Not for looking up individual victims'). What is missing is routing guidance against siblings — it never says, for example, whether this complements or should be paired with search_gdpr_fines or get_compliance_deadlines, so the agent must infer the boundary itself.

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