Fetch Dynatrace Davis AI problems filtered by Kubernetes namespace or status to quickly identify and troubleshoot cluster issues.
Fetch Dynatrace Davis AI problems, optionally filtered by Kubernetes namespace
| Name | Required | Description | Default |
|---|---|---|---|
| status | No | Problem status (default: OPEN) | |
| namespace | No | K8s namespace to filter problems (optional) | |
| time_from | No | Time range start (default: now-2h) |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full disclosure burden, and it only implies read-only behavior through the verb 'Fetch'. It says nothing about tenant/auth requirements, rate limits, pagination, or what a returned 'problem' contains, leaving real behavioral questions unanswered.
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?
A single front-loaded sentence with no filler, though the terseness comes at the cost of the behavioral detail the tool needs given it has no annotations.
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?
For a three-parameter, all-optional read tool with full schema coverage and no output schema, the description is marginally adequate, but it leaves the return shape and tenant/auth assumptions unexplained.
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 all three optional parameters are already documented in the schema, and the description only reiterates the namespace filter. It adds no interpretation of the status enum values or the default time window beyond what the schema states.
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?
States a specific verb ('Fetch') and precise resource ('Dynatrace Davis AI problems'), which no sibling tool duplicates, so an agent can distinguish it from the Kubernetes diagnostics in the sibling list. It stops short of explicitly pointing away from the closest relative, correlate_alerts_with_k8s.
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 phrase 'optionally filtered by Kubernetes namespace' implies the intended Kubernetes troubleshooting context, but the description never states when to prefer this over correlate_alerts_with_k8s or get_entity_health, nor any prerequisites such as a configured Dynatrace tenant.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.