observability_generate_grafana_provisioning

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses that the tool generates YAML files for datasource and dashboard provider, and explains the deployment effect. It does not mention any destructive actions, auth requirements, or side effects, but the tool appears non-destructive and the behavior is clearly described. The description provides sufficient behavioral context for an AI agent.

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 front-loaded with the primary purpose in the first line, followed by a concise paragraph explaining deployment behavior, then a clean list of parameters. Every sentence adds value without redundancy. The docstring-style 'Parameters' section is slightly verbose given the schema, but overall it is efficient and well-structured.

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 description explains the tool's output effect (YAML files for provisioning) and deployment steps. Although it does not describe the return value of the tool itself, the context signals indicate an output schema exists, so the description is not required to detail return format. Given the parameter count and simplicity of the tool, the description is adequately complete for an AI agent to understand usage.

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 3 parameters with defaults and titles but no property descriptions (schema description coverage 0%). The description compensates by listing each parameter with a brief explanation: 'URL Grafana should use to query Prometheus,' 'Grafana folder name dashboards land in,' and 'Filesystem path inside the Grafana container where dashboard JSONs are mounted.' This adds meaningful context beyond the schema's bare property names and defaults.

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 explicitly states 'Generate Grafana provisioning YAMLs (datasource + dashboard provider).' It specifies the verb 'Generate' and the resource 'Grafana provisioning YAMLs,' and further explains the purpose by describing the files' behavior when dropped into Grafana's provisioning directory. This clearly distinguishes it from sibling observability tools that generate dashboards or bundles.

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 explains how to deploy the generated files (drop into /etc/grafana/provisioning/) and what happens (auto-register datasource, auto-load dashboards). However, it does not explicitly state when to use this tool versus alternatives like observability_generate_dashboards or observability_generate_bundle. It lacks guidance on prerequisites or scenarios where this tool is preferred.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.