Generate an EU AI Act compliance report for an agent. Checks Art. 6 (risk), Art. 12 (audit trail), Art. 14 (human oversight), Art. 52 (transparency). No LLM cost.
| Name | Required | Description | Default |
|---|---|---|---|
| agent_id | Yes | The agent ID or name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint annotation, the description discloses the specific compliance articles it checks and notes 'No LLM cost', which is a behavioral trait (deterministic, no token usage). This adds useful context about what the report covers and its resource footprint.
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?
Two sentences, zero filler, and front-loaded with the action and object. Every word adds value, and it includes only necessary details like article references and cost behavior.
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 one required parameter, readOnlyHint annotation, and no output schema, the description fully covers what the tool does, which articles are checked, and the 'No LLM cost' behavior. There are no significant gaps for an agent to invoke it correctly.
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 schema provides 100% coverage for the sole parameter 'agent_id' with a clear description ('The agent ID or name'). The description adds no extra parameter semantics, but the schema fully handles it, so baseline 3 is appropriate.
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 the tool generates an EU AI Act compliance report for an agent, listing the specific articles checked (Art. 6, 12, 14, 52). This is a specific verb+resource combination that distinguishes it from all sibling tools, none of which handle compliance reporting.
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 implies the tool should be used when an EU AI Act compliance report is needed, but it does not explicitly state when to use it or exclude alternatives. There is no mention of alternative tools or prerequisites, so usage context is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes (create vs list vs get vs run vs connect). A few potential overlaps exist (deploy_template vs create_agent, import_agent vs create_agent, grade_prompt vs run_grading) but descriptions clarify the differences.
All tools share the 'kopern_' prefix and mostly follow a verb_noun pattern (create_*, get_*, list_*, run_*, connect_*). The exception is 'kopern_compliance_report', which uses a noun phrase without a verb, breaking the otherwise consistent naming.
With 31 tools, this exceeds the 25-tool threshold for well-scoped servers. While the domain is broad (agent lifecycle, grading, pipelines, teams, connectors), the sheer number of tools feels heavy and could be consolidated (e.g., a single 'manage_memory' tool already bundles multiple actions).
Core agent management (create, read, update, delete, list) is solid, and grading has suite creation, execution, and results. However, pipelines and teams lack get/update/delete operations, connectors only support connect (no disconnect), and there's no way to manage grading suites beyond creation and running. This leaves notable gaps for secondary resources.
