Check AI governance policies including model access, budget limits, data controls, and agent governance from the ThinkNEO gateway.
| Name | Required | Description | Default |
|---|---|---|---|
| workspace | No | Workspace name or ID | 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 and destructiveHint=false, and the description's 'Check' aligns with these. The description adds useful context by enumerating the governance areas covered (model access, budget limits, data controls, agent governance), which helps set expectations beyond 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 a single, well-structured sentence that front-loads the core action ('Check AI governance policies') and then specifies the scope. Every word adds value, with no redundancy or filler.
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?
The tool is simple (one optional parameter), annotations cover read-only/idempotent safety, and an output schema exists. The description adequately completes the picture by listing the key policy categories it covers, which is enough 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 input schema has 100% coverage for the single optional workspace parameter, including a description. The tool description does not add any additional parameter semantics beyond the schema, so the baseline score of 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 checks AI governance policies and lists specific domains (model access, budget limits, data controls, agent governance). It identifies a concrete resource and action, but does not explicitly differentiate it from sibling tools like thinkneo_check_spend or thinkneo_get_compliance_status.
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 gives no guidance on when to use this tool versus alternatives. It provides context ('from the ThinkNEO gateway') but does not mention exclusions, prerequisites, or when other tools like thinkneo_check_spend or thinkneo_a2a_policy would be more appropriate.
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.
