Generate a compliance report for regulatory frameworks (EU AI Act, ISO 42001, SOC2, NIST). Exports from live audit data.
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | Output format: ndjson or csv | ndjson |
| framework | No | Framework: eu-ai-act, iso-42001, soc2, nist | eu-ai-act |
| workspace | No | Workspace identifier | default |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds context about the data source ('live audit data'), which is useful for understanding freshness, but it does not disclose other behavioral aspects like authentication requirements or rate limits. This is acceptable given the annotations.
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 description is two sentences, front-loaded with the action and resource, and contains no redundant information. Every word contributes meaning.
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?
Given the output schema exists, annotations are present, and the input schema is well-documented, the description covers the essential aspects: purpose, frameworks, and data source. It could mention the return format or pagination behavior, but the output schema likely covers that, making the description sufficiently complete.
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 the schema already documents all three parameters. The description adds value by listing the valid frameworks for the 'framework' parameter, but it does not address 'format' or 'workspace' beyond what the schema provides. 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 a specific action ('Generate a compliance report') and identifies the resource scope (regulatory frameworks: EU AI Act, ISO 42001, SOC2, NIST). It distinguishes itself from sibling tools like thinkneo_get_compliance_status (status vs report) and thinkneo_audit_export (export vs generate) through its focus on generating reports from live audit data.
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 usage when a compliance report is needed for one of the listed frameworks, and notes the data source is live audit data. However, it does not explicitly state when to use this tool over alternatives, nor does it provide exclusions or prerequisites.
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.
Many tools have overlapping purposes, such as the multiple safety-check tools (thinkneo_check, thinkneo_detect_injection, thinkneo_evaluate_guardrail) and the many cost/reporting tools (thinkneo_agent_roi, thinkneo_decision_cost, thinkneo_business_impact). An agent would struggle to reliably pick the correct tool for a given intent. The boundaries between dashboard, audit, and reporting tools are particularly fuzzy.
All tools share the thinkneo_ prefix, but the remaining naming is inconsistent: some follow verb_noun (check_spend, list_alerts), some use noun_verb (compliance_generate, alert_rule_create), and others are bare nouns (business_impact, cache_status). This mix makes it hard to predict tool names based on action and object.
With 68 tools, this is an extremely large surface area, far beyond the typical 3-15 well-scoped set and even beyond the 25+ heavy threshold. Even for a comprehensive enterprise platform, the sheer number overwhelms an agent's ability to choose effectively. It feels like a kitchen-sink approach rather than a curated toolkit.
The toolset covers a wide range of governance, observability, and cost-management features, but there are notable lifecycle gaps: SLAs can be defined but not updated or deleted, alert rules lack an update operation, and registry entries have no remove/unpublish. Also, policy management is limited to checking, with no create/update tool. The memory tools feel out of place and lack a delete operation.
