Get semantic cache stats from the live gateway runtime metrics.
| 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. The description adds that the stats come from 'live gateway runtime metrics', which is useful context beyond annotations, but it does not disclose other behavioral traits such as refresh rate, potential latency, or data volume. This is similar to the get_calls example where annotations cover safety and description adds a scoping detail.
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 with no fluff. Every word contributes meaning: 'Get' + 'semantic cache stats' + 'live gateway runtime metrics' — exactly what the tool does.
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 zero parameters, a rich output schema, and comprehensive annotations, the description is nearly sufficient. It tells the user what data is returned (cache stats) and from where (live runtime metrics). It could add a note about what kind of stats (e.g., hit rate, size) but the output schema likely covers that, so this is complete enough.
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?
There are zero parameters, and schema coverage is 100%, so the baseline is 4 per the rubric. The description adds meaning by indicating the stats are 'semantic cache stats' from 'live gateway runtime metrics', but there are no parameters to elaborate on. It appropriately does not waste space explaining nonexistent parameters.
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 ('Get'), names the resource ('semantic cache stats'), and specifies the source ('live gateway runtime metrics'). It clearly distinguishes this from sibling tools like thinkneo_get_observability_dashboard, which sounds broader.
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 via 'live gateway runtime metrics' but does not explicitly state when to use this tool over alternatives, nor does it provide any exclusion criteria. It gives clear context (real-time cache stats) but no direct guidance on selecting this among the many observability-related siblings.
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.
