Check connector grants and secrets status from the gateway.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
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 known. The description adds useful context beyond annotations by specifying the object of the check ('connector grants and secrets') and the source ('gateway'), which helps set expectations without contradicting any 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, front-loaded sentence of nine words. Every word contributes meaning, with no filler or redundant detail. It is an model of conciseness.
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 parameters and an output schema present, the description is largely sufficient. It tells the agent what the tool does and from where. The only minor gap is that 'secrets status' could be slightly ambiguous, but the output schema likely mitigates this. Overall, it is complete for the tool's simplicity.
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 tool has zero parameters, so the schema covers everything. The rubric gives a baseline of 4 for 0 parameters, and the description does not need to add parameter details. It correctly focuses on the action and scope.
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 uses a specific verb ('Check') and clearly identifies the resource ('connector grants and secrets status') and origin ('from the gateway'). This distinguishes it from sibling tools like thinkneo_rotate_key (which mutates) and thinkneo_cache_status (which checks a different resource).
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 this tool is used when you need to inspect the status of connector grants and secrets, but it provides no explicit guidance on when to prefer it over alternative tools or any exclusions. With many sibling tools, more explicit context would be helpful, but the basic usage scenario is clear.
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.
